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Innovating the Personalization of Stratified Survivorship Care Pathways: Using a Cancer Data Ecosystem to Improve Care Access, Outcomes, Efficiency, and Costs

Innovating the Personalization of Stratified Survivorship Care Pathways: Using a Cancer Data... Abstract New models of survivorship care are needed that improve outcomes for the growing number of cancer survivors, address the increasing complexity of their health needs, and deal with the shortage of clinicians and rising costs of this care. Technology can aid the delivery of personalized, stratified survivorship care pathways where the intensity of care, the care setting, and the providers required for that care vary with survivors’ needs. Building a cancer data ecosystem of connected data streams that supports and learns from each patient can be used to streamline care, enhance efficiency, reduce costs, and facilitate research. This manuscript describes the input, analytics, and output components of the cancer data ecosystem that must be built and connected and also provides a real-world use case of how such a system could transform care in a large US comprehensive cancer center. Background The last decade has brought increased attention to the pending crisis in oncology and survivorship care as growing numbers of cancer survivors living years beyond their cancer and rising numbers of new patients diagnosed with cancer overwhelm the capacity of the limited number of clinicians available to treat them (1–6). Survivors need ongoing care to address toxicities in multiple organ systems (7) and impaired psychological health, cognition, social relationships, and financial well-being that can produce functional limitations and reduce quality of life (8). Clinician shortages are evident in oncology, primary care, nursing, and across the specialties needed to provide ongoing care for cancer-related issues (3–6). In addition to capacity, clinician knowledge gaps are also a problem: most clinicians, especially the primary care workforce, receive little training on how to care for the ongoing problems experienced by cancer survivors (9). An additional problem is the need to control the increasing costs to patients and families and to health-care systems and payers of cancer care and survivorship care (10–12). The lack of clinical time available to address survivors’ needs coupled with the lack of provider training in how to address these needs and patients’ inability to afford care is contributing to the continuing unmet needs reported by cancer survivors (13). New models of care delivery are needed to equitably improve patient outcomes, overcome workforce shortages and knowledge gaps, and constrain costs and burden on patients and care teams (14). Recent national efforts in the United States have identified 2 parts of a potential solution to create these new care models. First, there is growing recognition that the US needs to build new models of survivorship care that pivot care from “one size fits all” to more personalized, stratified survivorship care pathways where the intensity of care, the care setting, and the providers required for that care vary with survivors’ needs (15,16). Patients are triaged to 1 of 3 stepped-care pathways based on their overall risk and need profile determined by risk of recurrence, new cancers, or late effects, the severity of their ongoing cancer toxicities, their functional ability, and overall psychological health and ability to self-manage their health (17,18). Each pathway includes primary care, comorbidity management, and disease prevention then adds cancer-specific follow-up provided through 1) a major focus on supporting patients in self-managing their cancer specific needs outside of surveillance tests (low risk or low need), 2) a shared care model in which patients may be seen by a limited number of clinicians for cancer-related needs but otherwise self-manage their follow-up (moderate risk or moderate need), or 3) a complex case management approach where patients with high risk or needs are treated by a multidisciplinary clinical team (high risk or high need). This stratified survivorship care pathway model has been tested in England and Northern Ireland and been shown to meet patient needs while freeing up clinician time for higher acuity patients; improving care efficiency: both reducing wait times for oncology and improving the timeliness of follow-up tests; and reducing overall health-care costs (19). Although this model will need to be translated into US health-care delivery settings in different ways to reflect differences in care delivery structure, reimbursement, and other factors between the 2 countries, the current expert consensus is that working toward delivery of stratified survivorship care pathways in the United States is likely to help better meet the needs of our survivors while dealing with provider shortages and knowledge gaps and control costs (13,16,20). The second part of this solution to enable more efficient care and improve patient outcomes is to imbed a learning health-care system into care delivery. The learning health-care system works by engaging patients and providers in fully interoperable data systems to remotely monitor patients; generate predictive and prescriptive analytics to facilitate appropriate, timely referrals; and extend the reach of clinicians beyond clinic walls (14). The importance of the technological capabilities afforded by a learning health-care system was recently highlighted in a report on the key components responsible for the success of the stratified survivorship care models in the United Kingdom and Australia, which is also pivoting to adopt a similar model to the United Kingdom (15). This report points to the specific capabilities of the learning health-care system that are needed to enhance the acquisition and use of data to deliver and connect care, including methods to collect patient-reported outcome (PRO) and other patient-generated data so that patient issues can guide care; remote monitoring systems to allow for the detection of problems between clinic visits; algorithms that help triage patients to pathways; methods to support patients in self-management; ways to coordinate care and information exchange between oncology, primary care, specialists, and patients; and methods to engage all stakeholders and secure their ongoing buy-in (15). This involves a fundamental shift to deliver care outside of clinic walls to point of need, in patients’ homes, or in community settings as much as possible (20). It also involves continuous monitoring of care outcomes to trigger referrals to other care components that may be needed. To the extent this system is started at diagnosis, problems can be mitigated, improving downstream patient functioning and reducing the load on survivorship care. The Constituent Parts of a Cancer Data Ecosystem: Inputs, Analytics, and Outputs To transition to the future state where an embedded learning health-care system is enabling stratified survivorship care delivery, it is useful to map out the constituent parts of the system and understand how the inputs, analytics, and outputs work together in a cancer data ecosystem. As shown in Figure 1, this cancer data ecosystem must facilitate continuous collection and use of data from diagnosis forward, through treatment, follow-up, and detection of new issues (recurrences, cancers, late effects). Figure 1. Open in new tabDownload slide Components of a cancer data ecosystem: inputs, analytics, and outputs. EHR = electronic health record; PRO = patient-reported outcome. Figure 1. Open in new tabDownload slide Components of a cancer data ecosystem: inputs, analytics, and outputs. EHR = electronic health record; PRO = patient-reported outcome. System Inputs Data flow into the cancer data ecosystem from multiple sources, including patient-generated data, clinical data, and administrative data sources. Data systems must be built to collect electronic Patient-Reported Outcomes (ePROs) and other patient-generated data (eg, from wearables, sensors from glucose monitors, symptom or activity reporting apps, etc) for remote surveillance of symptoms and needs. These data must be continually updated to facilitate understanding of toxicities and care needs from new therapies. These same systems can elicit patient preferences for care and patient satisfaction with care. These patient-generated data can be linked to data on clinical care from the electronic health record (EHR), laboratory values, and imaging studies. Further data inputs include administrative sources, for example, from billing, quality, or clinical efficiency or use databases. Analytics For these data inputs to be of use, data systems must integrate patient-generated data, clinical data, and administrative data through analytics that connect these data to workflow so the data work in the service of patients and clinicians. These linked data should flow into algorithms and clinical decision support aids that inform tailored care recommendations, including for predicting risk (eg, of chronic and late effects or subsequent cancers); suggesting clinical trials or stepped care management strategies for risk reduction or toxicity mitigation; suggesting the appropriate intensity stratified follow-up care pathway; detecting recurrence or subsequent cancers; or suggesting whether telemedicine vs in-clinic visits are needed. Additionally, data can be linked into dashboards that help coordinate care among the multiple providers involved in a patient’s care and enable teamwork in medicine. System Outputs The outputs from analytics in the cancer data ecosystem can facilitate care processes, facilitate care delivery, and be used to facilitate administrative reporting. To improve care processes, care recommendations from the analytics should facilitate rather than substitute for productive conversations between patients and providers about appropriate care. For example, linked data on patient needs and patient preferences for their care can inform both provider-facing and patient-facing tools to facilitate patient-provider conversations and decision making about care. EHR alerts can flag high symptoms or overdue tests for clinicians, and prompts can help suggest needed care components—both of these can help prioritize issues to discuss during the limited time available for clinic visits. Patient portals or other digital tools can house online patient education material, patient-facing decision support tools, and dashboards of patients’ PROs or other outcomes that help engage patients in their care. These same tools can be used to promote patient adherence to recommended care. Once providers and patients have agreed on a course of care, the cancer data ecosystem can facilitate timely referrals to interventions for toxicity or symptom mitigation, functional restoration or maintenance, mental health, health promotion, and other needs. Care coordination provided by dashboards discussed above can help mend the disconnects in care that currently fragment care for patients. Technology can link providers from oncology, primary care, and specialty care for comanagement where needed. Technology can also facilitate connections to other members of the care team (eg, care managers, navigators) or interventions and resources in the community or public health settings. The cancer data ecosystem can also be used to increase patient access to care and extend the reach of the limited number of clinicians by using technology to facilitate care delivery at point of need, outside of clinic walls wherever possible. Telemedicine services and other remotely delivered care methods must be built to provide interventions when and where patients need them and in cost effective ways. Although some aspects of care cannot be delivered remotely, symptom management and health promotion interventions can be delivered via telemedicine in many cases. As care delivery systems have shifted their care to respond to the crisis caused by the COVID-19 pandemic, new models of telemedicine have been built almost overnight. These need to be tested for efficacy and continued after the pandemic ends for all those for whom it is effective. Technology also can be used to deliver self-management content and interventions to manage symptoms, change healthy behaviors, or proactively communicate with the care team. Self-management content and support can be built into individual patient portals or apps. Supporting patients in self-managing their health is critical to new models of survivorship care. Part of the success of the stratified survivorship care pathway model tested in the United Kingdom has been the improvements in care efficiency and reduced health-care costs achieved by helping patients self-manage their health to the extent possible to reduce unnecessary health-care use (19). Finally, outputs of the cancer data ecosystem can also serve to help health-care delivery systems facilitate clinic scheduling and billing or document quality reporting, accreditation standard reporting, results of quality improvement initiatives, or other metrics. With all of these data linked together, as envisioned by the National Cancer Institute (21), a connected cancer data ecosystem can be a powerful resource for research, creating a larger cancer health-care system that supports and learns from each patient. The data ecosystem can also be used to streamline care, enhance efficiency, and reduce costs. Use Care for Transforming Care With a Cancer Data Ecosystem: The MD Anderson Cancer Center (MDACC) Survivorship Program The potential for a cancer data ecosystem is best illustrated through a real-life example or use case. Here, we describe the efforts of MDACC’s survivorship program to build out a cancer data ecosystem. We highlight barriers and facilitators to this innovation and describe the anticipated efficiencies in practice with further build out of the cancer data ecosystem in the future. Overview of the MDACC Survivorship Clinic Program For over 10 years, MDACC’s Survivorship Program has provided long-term survivorship care for patients based on risk-stratified algorithms. Over 25,000 patients have transitioned care from their treating oncology team to this predominately advanced practice provider–led care model. The process for vetting each algorithm is rigorous and detailed. Each disease-specific committee is comprised of expert clinicians, researchers, oncologists, medical librarian, social workers, and nutritionists. The overall goal is to provide evidence-based and consensus-driven survivorship algorithms to guide systematic assessment and management of patients. Twelve individual clinics (10 diseased based, 1 treatment based, and 1 age based) offer survivorship care based on disease-specific algorithms founded on 4 care domains (22): 1) surveillance for late or recurrent malignancies, 2) late-effects monitoring and management, 3) risk reduction and cancer prevention strategies, and 4) psychosocial functioning for survivors. Details on the conceptual framework of the algorithms, their applications, implementation, and adoption in multidisciplinary disease-specific clinics have been published elsewhere (22). Briefly, quality improvement data show that provision of algorithm-concordant care is high, ranging from 70% to 100% depending on the disease site (23‐25). Additionally, survivors’ receptiveness of algorithm guidelines proposed by their provider is also high, ranging from 83% to 100% (26). Survivorship care occurs at the cancer center, including most imaging for cancer surveillance. Cancer screening and late-effects monitoring can be coordinated at the cancer center or in the patient’s local community through external providers. Survivorship providers typically follow patients on an annual basis and discuss the coordination of care with the patients, ensuring key health maintenance testing is planned. Implementing survivorship care models can be financially sustainable and provide a return on investment to organizations through a variety of mechanisms. Advanced practiced provider–based models can reduce expenses compared with physician-based models but allow advanced practice providers (APPs) to operate at the top their license (27,28). Transition of care also builds capacity for active treatment oncology clinics. Internal reviews of administrative data show that patients followed by a survivorship model of care are more likely to receive appropriate breast-imaging studies (29). MDACC’s ePRO Roadmap for Survivorship Care In 2005, clinics for long-term cancer survivors (ie, 5-15 years after diagnosis) became an essential component of patient care at MDACC. The system developed 12 site-specific clinics, and the use of PROs for clinical care among cancer survivors became an institutional priority. Historically, PROs such as data on symptoms, adverse effects, and overall health status had been used at MD Anderson to guide patient care at diagnosis, during treatment, and at follow-up visits for surveillance. In addition, PROs were collected for clinical trials and other types of research studies. To date, however, clear standards or consensus on which tool to use or on how to collect, analyze, or interpret data are lacking. For example, PRO data were collected using paper, telephones, and, most recently, electronically. MDACC has been working to establish a PROs clinical and research program that would enhance clinical outcomes and promote access to research among cancer survivors being cared for in our survivorship clinics. In 2018, executive and clinical leadership supported a project to develop a “PRO roadmap” for achieving standardized reporting across all 12 survivorship clinics. The development of this roadmap was to identify key stakeholders and experts in survivorship care and research. A multidisciplinary PRO team comprised of advanced practice providers (nurse practitioners and physician assistants), oncologists, general internists, family practice physicians, informatics analysts, PRO experts, and representatives from other disciplines provided oversight on the planning, implementation, and evaluation of this house-wide initiative. The primary goals were to 1) identify and select an appropriate PRO tool and framework to use across 12 survivorship clinics; 2) integrate ePROs into existing electronic systems, patient portal, and clinical workflow; and 3) conduct a pilot in 1 survivorship clinic to assess the feasibility of the implementation of the PRO framework. To date, 6 of the 12 survivorship clinics have submitted applications to collect PROs from survivors seen in their clinic. The PROs Committee vetted and approved the MD Anderson Symptom Inventory (MDASI) (core and site-specific modules) to integrate into the patient portal and the EHR across all clinics. Preliminary reports from providers in the clinic indicate patients with virtual visits complete the PRO tool at higher rates compared with patients who have in-person clinic visits. Several challenges related to the EHR system’s efficiency and retrieval of data have hindered reporting of success metrics. Specifically, patient adherence rates, patterns in symptom and interference severity, and the percent of incomplete surveys have been challenging to obtain. Identification of these technical bugs demonstrates the need for solutions and adjustments in how data are entered and managed (30). The current technological capabilities of this system and the potential capabilities and anticipated efficiencies in practice after further development are described in Table 1. Overall, MDACC’s ePROs Roadmap for Survivorship Care has spurred progress in building all of the elements of the cancer data ecosystem. Early results seem to be promising for the acceptance and use of the PRO roadmap in site-specific survivorship clinics. Patients and clinicians understand the relevance of using ePROs in promoting patient-centered care to enhance long-term survivorship care. PROs are assessed in some clinics and used to guide referrals to a multidisciplinary group of clinicians with specialized knowledge in the ongoing needs of cancer survivors. Providers have access to disease-specific algorithms that standardize care across the system. There is some interoperability among EHR platforms that allows for coordination of care across providers. There has been rapid integration of telemedicine delivery in response to the COVID-19 pandemic. Additionally, patients are supported with materials to help them self-manage their health in service lines such as physical activity and tobacco cessation. Table 1. Use case for transforming care with a cancer data ecosystem: MD Anderson Cancer Center's current and potential capabilities Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency EHR = electronic health record; PRO = patient-reported outcome. Open in new tab Table 1. Use case for transforming care with a cancer data ecosystem: MD Anderson Cancer Center's current and potential capabilities Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency EHR = electronic health record; PRO = patient-reported outcome. Open in new tab To continue this progress, further development of the cancer data ecosystem is needed. Full integration of PROs collected routinely with a survivor-specific tool would allow for ongoing monitoring of patients’ needs across time. Building a registry of patient toxicities and outcomes linked to clinical and administrative data would aid in the development of analytics to help guide the selection of the most efficient and effective prevention or mitigation strategies for a given patient. These should be integrated into patient-facing and clinician-facing clinical decision support tools to facilitate productive patient-provider decision making about care. Building fully interoperable EHR systems that allow providers to coordinate care and imbed automated referrals and tracking of patient outcomes will help streamline care and prevent patients from falling through the cracks. Expanding telemedicine capabilities and self-management content will help improve patient access to needed care and promote health equity. Finally, combining PROs with other institutional databases will allow for research and quality improvement initiatives to drive further care efficiencies and improve outcomes. Barriers and Facilitators to the ePROs Roadmap Barriers The ePRO team identified unique challenges associated with implementation of the PRO Roadmap in survivorship care and research. Despite the growing body of literature and research on cancer survivorship, the team found little published evidence on the barriers and facilitators related to the design of PRO programs for cohorts of long-term survivors. The PRO team identified several barriers to collection of PROs among long-term cancer survivors, including 1) most studies focused on certain cancer diagnoses and used cross-sectional designs; 2) there were few validated PRO tools in our institution, which could be used across diverse site-specific survivorship clinics; 3) institutional data collection PROs varied and included web-based, paper-and-pencil, and telephone calls; and 4) there were reports of noncompliance with collection and completion of PROs tools, respondent burden, and missing data. Other barriers identified for ePRO collection and use included limited access to information technology experts who could build the PROs into the EHRs, problems with linking and accessing the electronic medical record to the PRO survey and patient portal, and limited to no access among survivors to computers or other internet resources. Clinicians also provided feedback on challenges encountered, including 1) clinic workflow interruptions, 2) variation in symptoms requiring high alert values and their follow-up, and 3) obstacles to using the patient portal to collect ePROs. Facilitators The success of the ePRO initiative can be attributed to the support of the APPS in the 12 clinics who accepted the invitation to serve on the committee. There were early adopters and champions who enthusiastically volunteered their clinics to be pilot projects. They invested time, expertise, and clinical resources to write, submit, and present their PRO questionnaire applications to an in-house patient survey committee. Over the trajectory of their experience, they collected observational notes on the challenges encountered and the solutions developed to address the obstacles. They also provided detailed reports to their colleagues, served as mentors, and volunteered as coaches to other APPs. The success of the initiative has also been driven by key champions in leadership positions throughout the organization at the C-suite, department, outpatient center, and the advanced practice provider levels. It has been critical for these leaders to endorse and encourage provider uptake of new models of care. When dedicated providers carry the message of high-quality survivorship care, operational metrics improve for individual clinics. Further research is needed to understand the impact well-developed survivorship clinics can have on long-term patient outcomes. Barriers still exist in providing fully integrated survivorship care. Technology limitations through EHRs present challenges. Systems need to allow for accurate reporting to identify survivors within clinics, the development of automatically generated treatment summaries and survivorship care plans with integrated treatment decision support capabilities, and the ability to use technology to communicate seamlessly with external medical providers. These technology barriers cost valuable time for providers and hinder the ability to provide robust care. The use case of MDACC’s Survivorship Program demonstrates that despite the numerous challenges that exist in implementing an interoperable cancer data ecosystem, technological innovation is feasible and can enhance care when it is championed and incentivized. This use case also showcases how care may be facilitated through continued technological enhancements. For such data systems to be built more broadly, policy change is needed to overcome the barriers to their use. For example, remote monitoring of patients and telemedicine must be reimbursed by payers at rates that make these services feasible for cancer centers to provide. Mandates around the use of Health Insurance Portability and Accountability Act-compliant platforms for telemedicine must be relaxed because patients lack access to these platforms, or compliant platforms must be built into existing technology patients already own. Licensure requirements that clinicians cannot practice across state lines must be addressed in the context of telemedicine delivery. Finally, low-tech and high-tech strategies must be built to ensure that patients without broadband internet are not left out of new care models. Progress on addressing each of the barriers has been made in the months the United States has been dealing with the COVID-19 pandemic under temporary relaxation of regulations. However, reimbursement is complicated and inconsistent with numerous exceptions, and there is national concern that policy shifts made during the COVID-19 crisis will regress once the crisis subsides. What is needed now are long-term solutions that keep what is working or continue to reform policy where that is still needed. Research documenting return on investment from these technology innovations on outcomes at the level of the patient, clinic, and health-care system will be critical to championing the reforms needed. Finally, efforts that engage clinicians as well as patients and their families in the design and development of these systems are needed to ensure that they serve as the facilitators of care they aim to be. Notes Role of the funder: not applicable Author disclosures: the authors report no conflicts of interest Author contributions: Conceptualization: All authors; Data curation: All authors; Formal analysis: All authors; Visualization: CM Alfano, GR Palos, KR GIlmore; Writing original draft: All authors; Review and editing: All authors. Data Availability No new data were generated or analysed in support of this research. References 1 Bluethmann SM , Mariotto AB, Rowland JH. Anticipating the “silver tsunami”: prevalence trajectories and comorbidity burden among older cancer survivors in the United States . Cancer Epidemiol Biomarkers Prev . 2016 ; 25 ( 7 ): 1029 – 1036 . Google Scholar Crossref Search ADS PubMed WorldCat 2 Miller KD , Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019 . CA A Cancer J Clin . 2019 ; 69 ( 5 ): 363 – 385 . Google Scholar Crossref Search ADS WorldCat 3 Hortobagyi GN ; American Society of Clinical Oncology. A shortage of oncologists? The American Society of Clinical Oncology workforce study . J Clin Oncol . 2007 ; 25 ( 12 ): 1468 – 1469 . Google Scholar Crossref Search ADS PubMed WorldCat 4 Erikson C , Salsberg E, Forte G, et al. Future supply and demand for oncologists: challenges to assuring access to oncology services . J Oncol Pract . 2007 ; 3 ( 2 ): 79 – 86 . Google Scholar Crossref Search ADS PubMed WorldCat 5 American Society of Clinical Oncology. The state of cancer care in America. A report by the American Society of Clinical Oncology . J Oncol Pract . 2014 ; 10 ( 2 ): 119 – 142 . Crossref Search ADS PubMed WorldCat 6 Association of American Medical Colleges. The Complexities of Physician Supply and Demand: Projections From 2018 to 2033. 2020. https://www.aamc.org/media/45976/download. Accessed January 4, 2021. 7 Cupit-Link MC , Kirkland JL, Ness KK, et al. Biology of premature ageing in survivors of cancer . ESMO Open . 2017 ; 2 ( 5 ): e000250 . Google Scholar Crossref Search ADS PubMed WorldCat 8 Hewitt ME , Ganz PA, Institute of Medicine (U.S.), et al. From Cancer Patient to Cancer Survivor: Lost in Transition: An American Society of Clinical Oncology and Institute of Medicine Symposium . Washington, DC : National Academies Press ; 2006 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 9 Potosky AL , Han PK, Rowland J, et al. Differences between primary care physicians' and oncologists' knowledge, attitudes and practices regarding the care of cancer survivors . J Gen Intern Med . 2011 ; 26 ( 12 ): 1403 – 1410 . Google Scholar Crossref Search ADS PubMed WorldCat 10 Mariotto AB , Yabroff KR, Shao Y, et al. Projections of the cost of cancer care in the United States: 2010-2020 . J Natl Cancer Inst . 2011 ; 103 ( 2 ): 117 – 128 . Google Scholar Crossref Search ADS PubMed WorldCat 11 Zheng Z , Yabroff KR, Guy GP Jr, et al. Annual medical expenditure and productivity loss among colorectal, female breast, and prostate cancer survivors in the United States . J Natl Cancer Inst . 2016 ; 108 ( 5 ): djv382 . Google Scholar Crossref Search ADS WorldCat 12 Ekwueme DU , Zhao J, Rim SH, et al. Annual out-of-pocket expenditures and financial hardship among cancer survivors aged 18-64 years - United States, 2011-2016 . MMWR Morb Mortal Wkly Rep . 2019 ; 68 ( 22 ): 494 – 499 . Google Scholar Crossref Search ADS PubMed WorldCat 13 Kline RM , Arora NK, Bradley CJ, et al. Long-term survivorship care after cancer treatment - summary of a 2017 National Cancer Policy Forum Workshop . J Natl Cancer Inst . 2018 ; 110 ( 12 ): 1300 – 1310 . Google Scholar Crossref Search ADS PubMed WorldCat 14 Alfano CM , Mayer DK, Beckjord E, et al. Mending disconnects in cancer care: setting an agenda for research, practice, and policy . J Clin Oncol Clin Cancer Inform . 2020 ; 4 ( 4 ): 539 – 546 . Google Scholar OpenURL Placeholder Text WorldCat 15 Alfano CM , Jefford M, Maher J, et al. Building personalized cancer follow-up care pathways in the United States: lessons learned from implementation in England, Northern Ireland, and Australia . Am Soc Clin Oncol Educ Book . 2019 ; 39 ( 39 ): 625 – 639 . Google Scholar Crossref Search ADS PubMed WorldCat 16 Alfano CM , Mayer DK, Bhatia S, et al. Implementing personalized pathways for cancer follow-up care in the United States: proceedings from an American Cancer Society-American Society of Clinical Oncology summit . CA A Cancer J Clin . 2019 ; 69 ( 3 ): 234 – 247 . Google Scholar Crossref Search ADS WorldCat 17 Richards M , Corner J, Maher J. The National Cancer Survivorship Initiative: new and emerging evidence on the ongoing needs of cancer survivors . Br J Cancer . 2011 ; 105 ( S1 ): S1 – S4 . Google Scholar Crossref Search ADS PubMed WorldCat 18 Jefford M , Rowland J, Grunfeld E, et al. Implementing improved post-treatment care for cancer survivors in England, with reflections from Australia, Canada and the USA . Br J Cancer . 2013 ; 108 ( 1 ): 14 – 20 . Google Scholar Crossref Search ADS PubMed WorldCat 19 Macmillan Cancer Support. Evaluation of the Transforming Cancer Follow-up Programme in Northern Ireland Final Report. 2015 . https://www.macmillan.org.uk/documents/aboutus/research/researchandevaluationreports/ourresearchpartners/macmillantcfuevaluation-finalreport(260813).pdf. Accessed January 4, 2021. 20 Alfano CM , Leach CR, Smith TG, et al. Equitably improving outcomes for cancer survivors and supporting caregivers: a blueprint for care delivery, research, education, and policy . CA A Cancer J Clin . 2019 ; 69 ( 1 ): 35 – 49 . Google Scholar Crossref Search ADS WorldCat 21 National Cancer Institute. NCI cancer research data ecosystem infographic. https://www.cancer.gov/research/nci-role/bioinformatics/cancer-research-data-ecosystem-infographic. Accessed January 4, 2021. 22 Palos GR , Lewis-Patterson P, Gilmore K, et al. Changing nursing practice in survivorship care with clinical decision tools . Clin J Oncol Nurs . 2015 ; 19 ( 4 ): 482 – 484 , 488. Google Scholar Crossref Search ADS PubMed WorldCat 23 Palos GR , Lewis-Patterson PA, Gilmore KR, et al. Providers’ adherence to surveillance recommendations for colon and rectal cancer survivors . J Clin Oncol . 2016 ; 34 ( 15_suppl ): e21571 – e21571 . Google Scholar Crossref Search ADS WorldCat 24 Zandstra FA , Palos GR, Russell L, et al. Providers' concordance with survivorship clinical algorithms . J Clin Oncol . 2014 ; 32(31_suppl ): 243 . Google Scholar OpenURL Placeholder Text WorldCat 25 Gilmore KR , Palos GR, Chapman P, et al. Patterns of concordance with clinical algorithms for survivors of head and neck cancers . J Clin Oncol . 2018 ; 36(7_suppl ): 26 . Google Scholar OpenURL Placeholder Text WorldCat 26 Palos GR , Gilmore KR, Chapman P, et al. Surveillance of long-term colorectal cancer survivors . J Clin Oncol . 2015 ; 33(15_suppl ): e20596 . Google Scholar OpenURL Placeholder Text WorldCat 27 Gilmore KR , Palos GR, Chapman P, et al. Understanding the value of clinical survivorship programs . J Clin Oncol . 2018 ; 36(30_suppl ): 86 . Google Scholar OpenURL Placeholder Text WorldCat 28 Rodriguez MA , Palos GR, Gilmore KR, et al. Analysis of financial sustainability of survivorship clinics led by advanced practice providers . J Clin Oncol . 2019 ; 37(15_suppl ): 11560 . Google Scholar OpenURL Placeholder Text WorldCat 29 Palos G , Gilmore K, Chapman P, et al. HSR19-104: patterns of providers’ mammogram referrals for asymptomatic breast cancer survivors . J Natl Comprehen Cancer Netw . 2019 ; 17 ( 3.5 ): HSR19-104 . Google Scholar Crossref Search ADS WorldCat 30 Gilmore KR , Chapman P, Cleveland D, et al. Leveraging web-based interface systems with institutional databases to improve compliance with survivorship care plans . J Clin Oncol . 2016 ; 34(7_suppl ): 161 . Google Scholar OpenURL Placeholder Text WorldCat © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JNCI Monographs Oxford University Press

Innovating the Personalization of Stratified Survivorship Care Pathways: Using a Cancer Data Ecosystem to Improve Care Access, Outcomes, Efficiency, and Costs

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Publisher
Oxford University Press
Copyright
Copyright © 2021 Oxford University Press
ISSN
1052-6773
eISSN
1745-6614
DOI
10.1093/jncimonographs/lgaa011
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Abstract

Abstract New models of survivorship care are needed that improve outcomes for the growing number of cancer survivors, address the increasing complexity of their health needs, and deal with the shortage of clinicians and rising costs of this care. Technology can aid the delivery of personalized, stratified survivorship care pathways where the intensity of care, the care setting, and the providers required for that care vary with survivors’ needs. Building a cancer data ecosystem of connected data streams that supports and learns from each patient can be used to streamline care, enhance efficiency, reduce costs, and facilitate research. This manuscript describes the input, analytics, and output components of the cancer data ecosystem that must be built and connected and also provides a real-world use case of how such a system could transform care in a large US comprehensive cancer center. Background The last decade has brought increased attention to the pending crisis in oncology and survivorship care as growing numbers of cancer survivors living years beyond their cancer and rising numbers of new patients diagnosed with cancer overwhelm the capacity of the limited number of clinicians available to treat them (1–6). Survivors need ongoing care to address toxicities in multiple organ systems (7) and impaired psychological health, cognition, social relationships, and financial well-being that can produce functional limitations and reduce quality of life (8). Clinician shortages are evident in oncology, primary care, nursing, and across the specialties needed to provide ongoing care for cancer-related issues (3–6). In addition to capacity, clinician knowledge gaps are also a problem: most clinicians, especially the primary care workforce, receive little training on how to care for the ongoing problems experienced by cancer survivors (9). An additional problem is the need to control the increasing costs to patients and families and to health-care systems and payers of cancer care and survivorship care (10–12). The lack of clinical time available to address survivors’ needs coupled with the lack of provider training in how to address these needs and patients’ inability to afford care is contributing to the continuing unmet needs reported by cancer survivors (13). New models of care delivery are needed to equitably improve patient outcomes, overcome workforce shortages and knowledge gaps, and constrain costs and burden on patients and care teams (14). Recent national efforts in the United States have identified 2 parts of a potential solution to create these new care models. First, there is growing recognition that the US needs to build new models of survivorship care that pivot care from “one size fits all” to more personalized, stratified survivorship care pathways where the intensity of care, the care setting, and the providers required for that care vary with survivors’ needs (15,16). Patients are triaged to 1 of 3 stepped-care pathways based on their overall risk and need profile determined by risk of recurrence, new cancers, or late effects, the severity of their ongoing cancer toxicities, their functional ability, and overall psychological health and ability to self-manage their health (17,18). Each pathway includes primary care, comorbidity management, and disease prevention then adds cancer-specific follow-up provided through 1) a major focus on supporting patients in self-managing their cancer specific needs outside of surveillance tests (low risk or low need), 2) a shared care model in which patients may be seen by a limited number of clinicians for cancer-related needs but otherwise self-manage their follow-up (moderate risk or moderate need), or 3) a complex case management approach where patients with high risk or needs are treated by a multidisciplinary clinical team (high risk or high need). This stratified survivorship care pathway model has been tested in England and Northern Ireland and been shown to meet patient needs while freeing up clinician time for higher acuity patients; improving care efficiency: both reducing wait times for oncology and improving the timeliness of follow-up tests; and reducing overall health-care costs (19). Although this model will need to be translated into US health-care delivery settings in different ways to reflect differences in care delivery structure, reimbursement, and other factors between the 2 countries, the current expert consensus is that working toward delivery of stratified survivorship care pathways in the United States is likely to help better meet the needs of our survivors while dealing with provider shortages and knowledge gaps and control costs (13,16,20). The second part of this solution to enable more efficient care and improve patient outcomes is to imbed a learning health-care system into care delivery. The learning health-care system works by engaging patients and providers in fully interoperable data systems to remotely monitor patients; generate predictive and prescriptive analytics to facilitate appropriate, timely referrals; and extend the reach of clinicians beyond clinic walls (14). The importance of the technological capabilities afforded by a learning health-care system was recently highlighted in a report on the key components responsible for the success of the stratified survivorship care models in the United Kingdom and Australia, which is also pivoting to adopt a similar model to the United Kingdom (15). This report points to the specific capabilities of the learning health-care system that are needed to enhance the acquisition and use of data to deliver and connect care, including methods to collect patient-reported outcome (PRO) and other patient-generated data so that patient issues can guide care; remote monitoring systems to allow for the detection of problems between clinic visits; algorithms that help triage patients to pathways; methods to support patients in self-management; ways to coordinate care and information exchange between oncology, primary care, specialists, and patients; and methods to engage all stakeholders and secure their ongoing buy-in (15). This involves a fundamental shift to deliver care outside of clinic walls to point of need, in patients’ homes, or in community settings as much as possible (20). It also involves continuous monitoring of care outcomes to trigger referrals to other care components that may be needed. To the extent this system is started at diagnosis, problems can be mitigated, improving downstream patient functioning and reducing the load on survivorship care. The Constituent Parts of a Cancer Data Ecosystem: Inputs, Analytics, and Outputs To transition to the future state where an embedded learning health-care system is enabling stratified survivorship care delivery, it is useful to map out the constituent parts of the system and understand how the inputs, analytics, and outputs work together in a cancer data ecosystem. As shown in Figure 1, this cancer data ecosystem must facilitate continuous collection and use of data from diagnosis forward, through treatment, follow-up, and detection of new issues (recurrences, cancers, late effects). Figure 1. Open in new tabDownload slide Components of a cancer data ecosystem: inputs, analytics, and outputs. EHR = electronic health record; PRO = patient-reported outcome. Figure 1. Open in new tabDownload slide Components of a cancer data ecosystem: inputs, analytics, and outputs. EHR = electronic health record; PRO = patient-reported outcome. System Inputs Data flow into the cancer data ecosystem from multiple sources, including patient-generated data, clinical data, and administrative data sources. Data systems must be built to collect electronic Patient-Reported Outcomes (ePROs) and other patient-generated data (eg, from wearables, sensors from glucose monitors, symptom or activity reporting apps, etc) for remote surveillance of symptoms and needs. These data must be continually updated to facilitate understanding of toxicities and care needs from new therapies. These same systems can elicit patient preferences for care and patient satisfaction with care. These patient-generated data can be linked to data on clinical care from the electronic health record (EHR), laboratory values, and imaging studies. Further data inputs include administrative sources, for example, from billing, quality, or clinical efficiency or use databases. Analytics For these data inputs to be of use, data systems must integrate patient-generated data, clinical data, and administrative data through analytics that connect these data to workflow so the data work in the service of patients and clinicians. These linked data should flow into algorithms and clinical decision support aids that inform tailored care recommendations, including for predicting risk (eg, of chronic and late effects or subsequent cancers); suggesting clinical trials or stepped care management strategies for risk reduction or toxicity mitigation; suggesting the appropriate intensity stratified follow-up care pathway; detecting recurrence or subsequent cancers; or suggesting whether telemedicine vs in-clinic visits are needed. Additionally, data can be linked into dashboards that help coordinate care among the multiple providers involved in a patient’s care and enable teamwork in medicine. System Outputs The outputs from analytics in the cancer data ecosystem can facilitate care processes, facilitate care delivery, and be used to facilitate administrative reporting. To improve care processes, care recommendations from the analytics should facilitate rather than substitute for productive conversations between patients and providers about appropriate care. For example, linked data on patient needs and patient preferences for their care can inform both provider-facing and patient-facing tools to facilitate patient-provider conversations and decision making about care. EHR alerts can flag high symptoms or overdue tests for clinicians, and prompts can help suggest needed care components—both of these can help prioritize issues to discuss during the limited time available for clinic visits. Patient portals or other digital tools can house online patient education material, patient-facing decision support tools, and dashboards of patients’ PROs or other outcomes that help engage patients in their care. These same tools can be used to promote patient adherence to recommended care. Once providers and patients have agreed on a course of care, the cancer data ecosystem can facilitate timely referrals to interventions for toxicity or symptom mitigation, functional restoration or maintenance, mental health, health promotion, and other needs. Care coordination provided by dashboards discussed above can help mend the disconnects in care that currently fragment care for patients. Technology can link providers from oncology, primary care, and specialty care for comanagement where needed. Technology can also facilitate connections to other members of the care team (eg, care managers, navigators) or interventions and resources in the community or public health settings. The cancer data ecosystem can also be used to increase patient access to care and extend the reach of the limited number of clinicians by using technology to facilitate care delivery at point of need, outside of clinic walls wherever possible. Telemedicine services and other remotely delivered care methods must be built to provide interventions when and where patients need them and in cost effective ways. Although some aspects of care cannot be delivered remotely, symptom management and health promotion interventions can be delivered via telemedicine in many cases. As care delivery systems have shifted their care to respond to the crisis caused by the COVID-19 pandemic, new models of telemedicine have been built almost overnight. These need to be tested for efficacy and continued after the pandemic ends for all those for whom it is effective. Technology also can be used to deliver self-management content and interventions to manage symptoms, change healthy behaviors, or proactively communicate with the care team. Self-management content and support can be built into individual patient portals or apps. Supporting patients in self-managing their health is critical to new models of survivorship care. Part of the success of the stratified survivorship care pathway model tested in the United Kingdom has been the improvements in care efficiency and reduced health-care costs achieved by helping patients self-manage their health to the extent possible to reduce unnecessary health-care use (19). Finally, outputs of the cancer data ecosystem can also serve to help health-care delivery systems facilitate clinic scheduling and billing or document quality reporting, accreditation standard reporting, results of quality improvement initiatives, or other metrics. With all of these data linked together, as envisioned by the National Cancer Institute (21), a connected cancer data ecosystem can be a powerful resource for research, creating a larger cancer health-care system that supports and learns from each patient. The data ecosystem can also be used to streamline care, enhance efficiency, and reduce costs. Use Care for Transforming Care With a Cancer Data Ecosystem: The MD Anderson Cancer Center (MDACC) Survivorship Program The potential for a cancer data ecosystem is best illustrated through a real-life example or use case. Here, we describe the efforts of MDACC’s survivorship program to build out a cancer data ecosystem. We highlight barriers and facilitators to this innovation and describe the anticipated efficiencies in practice with further build out of the cancer data ecosystem in the future. Overview of the MDACC Survivorship Clinic Program For over 10 years, MDACC’s Survivorship Program has provided long-term survivorship care for patients based on risk-stratified algorithms. Over 25,000 patients have transitioned care from their treating oncology team to this predominately advanced practice provider–led care model. The process for vetting each algorithm is rigorous and detailed. Each disease-specific committee is comprised of expert clinicians, researchers, oncologists, medical librarian, social workers, and nutritionists. The overall goal is to provide evidence-based and consensus-driven survivorship algorithms to guide systematic assessment and management of patients. Twelve individual clinics (10 diseased based, 1 treatment based, and 1 age based) offer survivorship care based on disease-specific algorithms founded on 4 care domains (22): 1) surveillance for late or recurrent malignancies, 2) late-effects monitoring and management, 3) risk reduction and cancer prevention strategies, and 4) psychosocial functioning for survivors. Details on the conceptual framework of the algorithms, their applications, implementation, and adoption in multidisciplinary disease-specific clinics have been published elsewhere (22). Briefly, quality improvement data show that provision of algorithm-concordant care is high, ranging from 70% to 100% depending on the disease site (23‐25). Additionally, survivors’ receptiveness of algorithm guidelines proposed by their provider is also high, ranging from 83% to 100% (26). Survivorship care occurs at the cancer center, including most imaging for cancer surveillance. Cancer screening and late-effects monitoring can be coordinated at the cancer center or in the patient’s local community through external providers. Survivorship providers typically follow patients on an annual basis and discuss the coordination of care with the patients, ensuring key health maintenance testing is planned. Implementing survivorship care models can be financially sustainable and provide a return on investment to organizations through a variety of mechanisms. Advanced practiced provider–based models can reduce expenses compared with physician-based models but allow advanced practice providers (APPs) to operate at the top their license (27,28). Transition of care also builds capacity for active treatment oncology clinics. Internal reviews of administrative data show that patients followed by a survivorship model of care are more likely to receive appropriate breast-imaging studies (29). MDACC’s ePRO Roadmap for Survivorship Care In 2005, clinics for long-term cancer survivors (ie, 5-15 years after diagnosis) became an essential component of patient care at MDACC. The system developed 12 site-specific clinics, and the use of PROs for clinical care among cancer survivors became an institutional priority. Historically, PROs such as data on symptoms, adverse effects, and overall health status had been used at MD Anderson to guide patient care at diagnosis, during treatment, and at follow-up visits for surveillance. In addition, PROs were collected for clinical trials and other types of research studies. To date, however, clear standards or consensus on which tool to use or on how to collect, analyze, or interpret data are lacking. For example, PRO data were collected using paper, telephones, and, most recently, electronically. MDACC has been working to establish a PROs clinical and research program that would enhance clinical outcomes and promote access to research among cancer survivors being cared for in our survivorship clinics. In 2018, executive and clinical leadership supported a project to develop a “PRO roadmap” for achieving standardized reporting across all 12 survivorship clinics. The development of this roadmap was to identify key stakeholders and experts in survivorship care and research. A multidisciplinary PRO team comprised of advanced practice providers (nurse practitioners and physician assistants), oncologists, general internists, family practice physicians, informatics analysts, PRO experts, and representatives from other disciplines provided oversight on the planning, implementation, and evaluation of this house-wide initiative. The primary goals were to 1) identify and select an appropriate PRO tool and framework to use across 12 survivorship clinics; 2) integrate ePROs into existing electronic systems, patient portal, and clinical workflow; and 3) conduct a pilot in 1 survivorship clinic to assess the feasibility of the implementation of the PRO framework. To date, 6 of the 12 survivorship clinics have submitted applications to collect PROs from survivors seen in their clinic. The PROs Committee vetted and approved the MD Anderson Symptom Inventory (MDASI) (core and site-specific modules) to integrate into the patient portal and the EHR across all clinics. Preliminary reports from providers in the clinic indicate patients with virtual visits complete the PRO tool at higher rates compared with patients who have in-person clinic visits. Several challenges related to the EHR system’s efficiency and retrieval of data have hindered reporting of success metrics. Specifically, patient adherence rates, patterns in symptom and interference severity, and the percent of incomplete surveys have been challenging to obtain. Identification of these technical bugs demonstrates the need for solutions and adjustments in how data are entered and managed (30). The current technological capabilities of this system and the potential capabilities and anticipated efficiencies in practice after further development are described in Table 1. Overall, MDACC’s ePROs Roadmap for Survivorship Care has spurred progress in building all of the elements of the cancer data ecosystem. Early results seem to be promising for the acceptance and use of the PRO roadmap in site-specific survivorship clinics. Patients and clinicians understand the relevance of using ePROs in promoting patient-centered care to enhance long-term survivorship care. PROs are assessed in some clinics and used to guide referrals to a multidisciplinary group of clinicians with specialized knowledge in the ongoing needs of cancer survivors. Providers have access to disease-specific algorithms that standardize care across the system. There is some interoperability among EHR platforms that allows for coordination of care across providers. There has been rapid integration of telemedicine delivery in response to the COVID-19 pandemic. Additionally, patients are supported with materials to help them self-manage their health in service lines such as physical activity and tobacco cessation. Table 1. Use case for transforming care with a cancer data ecosystem: MD Anderson Cancer Center's current and potential capabilities Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency EHR = electronic health record; PRO = patient-reported outcome. Open in new tab Table 1. Use case for transforming care with a cancer data ecosystem: MD Anderson Cancer Center's current and potential capabilities Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency Cancer data ecosystem component . MD Anderson: current capabilities . MD Anderson: potential capabilities . Inputs Collect PROs and other patient-generated data for remote monitoring of symptoms and needs that guide care in addition to EHR (labs, imaging) and administrative data (billing, quality metrics, and efficiency data) PROs guide care: collection implemented in some clinics, 1-time collection before appointment Full data integration in all clinics Survivorship-specific PRO tool accurately captures needs Standardized recurrent remote monitoring facilitates ongoing monitoring and provider intervention Analytics Integrate patient-generated, clinical, and administrative data into algorithms and clinical decision support aids to: Predict risk of chronic and late treatment effects and needs Identify patients for clinical trials (first-line and ongoing) Suggest appropriate strategies for prevention or mitigation of acute, chronic, and late treatment effects: identify symptoms specific to each cancer diagnosis that warrant high alerts, set the cutpoint, and plan of action on how to identify alert in real-time and provide prompt follow-up Suggest stratified follow-up care pathways Detect recurrence or subsequent cancers Suggest telemedicine vs in-clinic appointments Create dashboards for care coordination Provider online access to algorithms linked to specific components of follow-up care standardize care for site-specific cancers Some shared EHR platforms allow for coordination among providers Registry of outcomes in cohorts of long-term cancer survivors allows for real-time understanding of chronic and late effects and care needs Linked institutional databases facilitate acquisition of data needed for research, eg, demographics, clinical, financial, and system variables that would point to risk identification and reduction strategies High-alert symptom strategies rapidly identify patients with acute needs Fully interoperable EHR platform coordinates care across disciplines Outputs Data analytics facilitate care processes: PROs and EHR-embedded decision support tools engage patients, facilitate patient–provider communication and decision making and adherence to care Analytics point to appropriate, timely referrals to stepped care and in-person vs telemedicine interventions for toxicity or symptom mitigation, functional restoration, mental health, health promotion, and other needs Dashboards and interoperability support knowledge transfer and care coordination among oncology, primary care, and specialty care providers to enable effective teamwork in health care Dashboards facilitate connections and comanagement with navigators, care managers, community, and public health resources PROs are used during on-site clinic appointments to link patients to multidisciplinary clinicians who have the knowledge and experience to specialize in caring for patients diagnosed with a particular cancer During a survivor’s visit, if findings warrant, appointments are made on the same day to minimize need to return on another day Referrals are made outside the institution for patients living great distances from the institutions or who prefer to have their care in community-based facilities Ability to share documents enhances coordination of care for some patients on shared EHR platforms or where external providers opt-in Coordination done on an individual basis by medical provider Algorithms automatically link patients to needed interventions and track referral outcomes to ensure patients do not fall through cracks Data on patient preferences and clinical needs inform choice of clinic visit, virtual visit, or telephone conversation Fully interoperable EHR platform allowing all providers to access information and coordinate care Analytics and technology facilitate care delivery through: Provision of telemedicine care interventions at point of need, outside of clinic walls, wherever feasible Supporting patients and their caregivers in self-management and risk reduction and health promotion activities (including online patient education materials to educate patients on PROs, eg, role of PROs in their clinical care and research studies, importance of completion, use of high-alerts) Rapid integration of telemedicine as part of COVID-19 response Self-management materials are available for some service lines (eg, physical activity and tobacco cessation) Telemedicine access is possible wherever needed across disciplines and in a variety of formats to promote health equity and patient access Technology facilitates virtual consultations, referrals, or support with navigator; managers, community, and public health resources, outside of clinical space and time where possible Expanded educational content helps patients self-manage in all service lines and self-management strategies Connected data systems facilitate administrative data collection and use and care efficiency for scheduling, billing, quality reporting, meeting accreditation standards, patient outcome assessment, and patient satisfaction Major challenges in linking all the institutional databases so entry and access to data can be easily completed Linked institutional databases and registry of patient outcomes in cohorts of long-term cancer survivors facilitate acquisition of data needed for research, testing quality improvement initiatives, and enhancing efficiency EHR = electronic health record; PRO = patient-reported outcome. Open in new tab To continue this progress, further development of the cancer data ecosystem is needed. Full integration of PROs collected routinely with a survivor-specific tool would allow for ongoing monitoring of patients’ needs across time. Building a registry of patient toxicities and outcomes linked to clinical and administrative data would aid in the development of analytics to help guide the selection of the most efficient and effective prevention or mitigation strategies for a given patient. These should be integrated into patient-facing and clinician-facing clinical decision support tools to facilitate productive patient-provider decision making about care. Building fully interoperable EHR systems that allow providers to coordinate care and imbed automated referrals and tracking of patient outcomes will help streamline care and prevent patients from falling through the cracks. Expanding telemedicine capabilities and self-management content will help improve patient access to needed care and promote health equity. Finally, combining PROs with other institutional databases will allow for research and quality improvement initiatives to drive further care efficiencies and improve outcomes. Barriers and Facilitators to the ePROs Roadmap Barriers The ePRO team identified unique challenges associated with implementation of the PRO Roadmap in survivorship care and research. Despite the growing body of literature and research on cancer survivorship, the team found little published evidence on the barriers and facilitators related to the design of PRO programs for cohorts of long-term survivors. The PRO team identified several barriers to collection of PROs among long-term cancer survivors, including 1) most studies focused on certain cancer diagnoses and used cross-sectional designs; 2) there were few validated PRO tools in our institution, which could be used across diverse site-specific survivorship clinics; 3) institutional data collection PROs varied and included web-based, paper-and-pencil, and telephone calls; and 4) there were reports of noncompliance with collection and completion of PROs tools, respondent burden, and missing data. Other barriers identified for ePRO collection and use included limited access to information technology experts who could build the PROs into the EHRs, problems with linking and accessing the electronic medical record to the PRO survey and patient portal, and limited to no access among survivors to computers or other internet resources. Clinicians also provided feedback on challenges encountered, including 1) clinic workflow interruptions, 2) variation in symptoms requiring high alert values and their follow-up, and 3) obstacles to using the patient portal to collect ePROs. Facilitators The success of the ePRO initiative can be attributed to the support of the APPS in the 12 clinics who accepted the invitation to serve on the committee. There were early adopters and champions who enthusiastically volunteered their clinics to be pilot projects. They invested time, expertise, and clinical resources to write, submit, and present their PRO questionnaire applications to an in-house patient survey committee. Over the trajectory of their experience, they collected observational notes on the challenges encountered and the solutions developed to address the obstacles. They also provided detailed reports to their colleagues, served as mentors, and volunteered as coaches to other APPs. The success of the initiative has also been driven by key champions in leadership positions throughout the organization at the C-suite, department, outpatient center, and the advanced practice provider levels. It has been critical for these leaders to endorse and encourage provider uptake of new models of care. When dedicated providers carry the message of high-quality survivorship care, operational metrics improve for individual clinics. Further research is needed to understand the impact well-developed survivorship clinics can have on long-term patient outcomes. Barriers still exist in providing fully integrated survivorship care. Technology limitations through EHRs present challenges. Systems need to allow for accurate reporting to identify survivors within clinics, the development of automatically generated treatment summaries and survivorship care plans with integrated treatment decision support capabilities, and the ability to use technology to communicate seamlessly with external medical providers. These technology barriers cost valuable time for providers and hinder the ability to provide robust care. The use case of MDACC’s Survivorship Program demonstrates that despite the numerous challenges that exist in implementing an interoperable cancer data ecosystem, technological innovation is feasible and can enhance care when it is championed and incentivized. This use case also showcases how care may be facilitated through continued technological enhancements. For such data systems to be built more broadly, policy change is needed to overcome the barriers to their use. For example, remote monitoring of patients and telemedicine must be reimbursed by payers at rates that make these services feasible for cancer centers to provide. Mandates around the use of Health Insurance Portability and Accountability Act-compliant platforms for telemedicine must be relaxed because patients lack access to these platforms, or compliant platforms must be built into existing technology patients already own. Licensure requirements that clinicians cannot practice across state lines must be addressed in the context of telemedicine delivery. Finally, low-tech and high-tech strategies must be built to ensure that patients without broadband internet are not left out of new care models. Progress on addressing each of the barriers has been made in the months the United States has been dealing with the COVID-19 pandemic under temporary relaxation of regulations. However, reimbursement is complicated and inconsistent with numerous exceptions, and there is national concern that policy shifts made during the COVID-19 crisis will regress once the crisis subsides. What is needed now are long-term solutions that keep what is working or continue to reform policy where that is still needed. Research documenting return on investment from these technology innovations on outcomes at the level of the patient, clinic, and health-care system will be critical to championing the reforms needed. Finally, efforts that engage clinicians as well as patients and their families in the design and development of these systems are needed to ensure that they serve as the facilitators of care they aim to be. Notes Role of the funder: not applicable Author disclosures: the authors report no conflicts of interest Author contributions: Conceptualization: All authors; Data curation: All authors; Formal analysis: All authors; Visualization: CM Alfano, GR Palos, KR GIlmore; Writing original draft: All authors; Review and editing: All authors. Data Availability No new data were generated or analysed in support of this research. References 1 Bluethmann SM , Mariotto AB, Rowland JH. Anticipating the “silver tsunami”: prevalence trajectories and comorbidity burden among older cancer survivors in the United States . Cancer Epidemiol Biomarkers Prev . 2016 ; 25 ( 7 ): 1029 – 1036 . Google Scholar Crossref Search ADS PubMed WorldCat 2 Miller KD , Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019 . CA A Cancer J Clin . 2019 ; 69 ( 5 ): 363 – 385 . Google Scholar Crossref Search ADS WorldCat 3 Hortobagyi GN ; American Society of Clinical Oncology. 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J Clin Oncol . 2016 ; 34(7_suppl ): 161 . Google Scholar OpenURL Placeholder Text WorldCat © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

JNCI MonographsOxford University Press

Published: Sep 3, 2021

Keywords: critical pathways; ecosystems; cancer; survivors; cancer care facilities; cancer survivors

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