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Patient-reported outcomes—symptoms, treatment side effects, and health-related quality of life—are important to consider in chronic illness care. The increasing availability of health IT to collect patient-reported outcomes and integrate results within the electronic health record provides an unprecedented opportunity to support patients’ symptom monitoring, shared decision-making, and effective use of the health care system.
The objectives of this study are to co-design a dashboard that displays patient-reported outcomes along with other clinical data (eg, laboratory tests, medications, and appointments) within an electronic health record and conduct a longitudinal demonstration trial to evaluate whether the dashboard is associated with improved shared decision-making and disease management outcomes.
Co-design teams comprising study investigators, patients with advanced cancer or chronic kidney disease, their care partners, and their clinicians will collaborate to develop the dashboard. Investigators will work with clinic staff to implement the co-designed dashboard for clinical testing during a demonstration trial. The primary outcome of the demonstration trial is whether the quality of shared decision-making increases from baseline to the 3-month follow-up. Secondary outcomes include longitudinal changes in satisfaction with care, self-efficacy in managing treatments and symptoms, health-related quality of life, and use of costly and potentially avoidable health care services. Implementation outcomes (ie, fidelity, appropriateness, acceptability, feasibility, reach, adoption, and sustainability) during the co-design process and demonstration trial will also be collected and summarized.
The dashboard co-design process was completed in May 2020, and data collection for the demonstration trial is anticipated to be completed by the end of July 2022. The results will be disseminated in at least one manuscript per study objective.
This protocol combines stakeholder engagement, health care coproduction frameworks, and health IT to develop a clinically feasible model of person-centered care delivery. The results will inform our current understanding of how best to integrate patient-reported outcome measures into clinical workflows to improve outcomes and reduce the burden of chronic disease on patients and health care systems.
DERR1-10.2196/38461
Over half of Americans are currently living with a chronic illness such as cancer or chronic kidney disease [
Dartmouth coproduction model for clinical integration of patient-reported outcome measures (adapted from Nelson et al [
Efforts to improve care delivery for advanced cancer and chronic kidney disease offer strong opportunities to develop and evaluate strategies for implementing patient-reported outcome measures. Medical advances in treatments have made it possible to live months or years with these diagnoses, meaning that patients need to manage their symptoms and treatments on a regular basis and across ongoing health care encounters [
Given these benefits, patient-reported outcome data can enhance the health care
This paper details the protocol for our study. We will design, implement, and longitudinally evaluate a clinician- and patient-facing dashboard for displaying patient-reported outcomes and other data from the electronic health record. The dashboard will be co-designed by patients and care partners, clinicians, and researchers to optimize the likelihood that it will be acceptable, feasible, and effective [
This study has two objectives: (1) to co-design a clinical dashboard that integrates patient-reported outcomes with other clinical data (eg, vital signs, laboratory tests, and medications) through the electronic health record and (2) to conduct a longitudinal demonstration trial evaluating whether the dashboard is associated with improved shared decision-making and disease management outcomes. Within objective 2, we hypothesize that patients who use the dashboard will experience increases from baseline to the 3- and 6-month follow-ups in perceived quality of shared decision-making, satisfaction with care, self-efficacy in managing treatments and symptoms, and health-related quality of life. We also hypothesize that, compared with historically and demographically matched controls, patients who use the dashboard will have a greater reduction in use of potentially avoidable, high-cost, and low-value health care services (see the Methods section for specific indicators that will be tested). By carefully delineating the dashboard design and evaluation process, this study will enable the dashboard’s scalability and inform future investigations aiming to adapt the patient-reported outcome dashboard to other health care systems.
The Northwestern University Institutional Review Board approved all procedures described in this manuscript and a detailed data security plan (STU00210091, STU00211654, and STU00212634). Depending on the assessment point, survey data will be collected and stored securely through the health care system’s electronic patient portal or through a REDCap (Research Electronic Data Capture; Vanderbilt University) [
The dashboard will build on an existing infrastructure called the Northwestern Medicine Patient-Reported Outcomes system. This is the health care system’s current technological framework for administering patient-reported outcome measures electronically and integrating data into the electronic health record to inform clinical care delivery [
In this study, this system will be adapted, with patient and other stakeholder input, into a clinical dashboard to facilitate symptom management and shared decision-making during scheduled health care visits for patients with advanced cancer and chronic kidney disease. Patient and care partner input will maximize the dashboard’s patient-centeredness. By including clinicians in the design process, attention will be paid to the possible impact of dashboard participation on digital fatigue. To direct attention to and encourage discussion of the more potentially bothersome “hard-stop” alerts, alert thresholds on the patient-reported outcome measures will be set relatively high. The dashboard will be viewable by both patients (via the electronic patient portal) and physicians (via the health system’s electronic health record software) and will automatically populate electronic health record–linked data, including patient-reported outcomes, laboratory test results, medications, and vital signs.
The first objective of the study is to collaborate with stakeholders who represent “end users” to co-design our clinical dashboard. End users include patients with advanced cancer or advanced chronic kidney disease, their care partners, and their clinicians. Clinician participants will include 2 oncologists (specializing in the treatment of gastrointestinal and lung cancer), a nephrologist, a nephrology physician assistant, and 2 primary care physicians. Patients will be eligible to participate if they have a history of receiving care from one or more of the participating clinicians at Northwestern Memorial Health Care. Patients with gastrointestinal cancer must have a confirmed diagnosis of stage 4 gastrointestinal cancer and have been receiving intravenous chemotherapy for at least three months. Patients with lung cancer must have a confirmed diagnosis of stage 3C or 4 lung cancer and have been receiving first- or second-line chemotherapy for at least 3 months. Patients with chronic kidney disease must have a confirmed diagnosis of at least stage 3 (defined as a clinical diagnosis or an estimated glomerular filtration rate of <60). Care partners will be eligible to participate if they assist in the care of a patient who would meet the inclusion criteria.
For each of the two disease groups of interest (advanced cancer and chronic kidney disease), we will convene a co-design team comprising approximately 20 highly engaged stakeholders, including investigators, patients, care partners, clinicians, and health IT professionals. Co-design teams will iteratively develop the dashboard over the course of monthly meetings held during the first year of the study. Co-design meetings will involve team-based working sessions with predefined objectives and deliverables for each meeting (
Detailed outline of dashboard co-design session objectives.
Activity | Co-design phase ( |
Objectives |
Co-design launch meeting | 1 |
Introduction to the project and key concepts Review of co-design working structure and scope of work |
Working session 1 | 1 and 2 |
Team building Identifying facilitators of and barriers to shared decision-making and health coproduction Exploring how better sharing of information can help |
Working session 2 | 2 |
Developing an understanding of how information can address facilitators of and barriers to improving care management |
Working session 3 | 2 and 3 |
Identifying common themes, priorities, and values regarding information and shared decision-making of dashboard end users Developing team-specific co-design objectives |
Working session 4 | 1, 2, and 3 |
Refining team-specific objectives Introducing options for data elements to populate dashboards Envisioning dashboard |
Working session 5 | 2, 3, and 4 |
Exploring the use of a dashboard in a case example to advance emerging dashboard concepts |
Working session 6 | 2, 3, and 4 |
Exploring the use of a dashboard in a case example focused on a point of shared decision-making to advance emerging dashboard concepts |
Working session 7 | 2 and 3 |
Defining priority dashboard elements by dashboard user type Proposing questions for external validation (focus groups) Exchanging ideas and plans for dashboard concept between cancer and kidney disease teams |
Working session 8 | 2 and 3 |
Reviewing dashboard drafts and confirming alignment with co-design teams’ visions Reviewing data sources and measures to populate dashboards |
Working session 9 | 2 and 3 |
Reviewing feasible dashboard display options Confirming completeness and appropriateness of planned data elements |
Working session 10 | 2 and 3 |
Demonstration of programmed dashboard display Cancer and kidney disease co-design teams present respective dashboards and exchange ideas. |
Working session 11 | 3 and 4 |
Demonstration and critical review of fully programmed dashboards |
Working session 12 | 2 and 3 |
Interactive demonstration of fully programmed dashboards and questionnaires Establishing specifications for alerts (symptom thresholds and routing) Determining communication strategy and framing of patient-facing questionnaires |
Working session 13 (physician champion working meeting) | 2, 3, and 4 |
Confirming final dashboard specifications Demonstration and discussion of in-basket alerts Confirming final patient dashboard user criteria Confirming implementation workflows |
Co-design wrap-up meeting | 4 |
Live demonstration of the prefinal dashboards and questionnaires Conducting a reflection on the entire design process with respect to participation in co-design activities Examining and discussing implications of COVID-19 and considerations for telehealth |
In parallel to these intensive co-design team meetings, we will also hold supplemental focus groups. The primary purpose of these focus groups is to validate the evolving consensus and work product of the co-design teams with additional stakeholders. In tandem with phases 1 and 2 of co-design, we will conduct a focus group with 24 patients and care partners to gather an initial assessment of attitudes, beliefs, and perceptions to inform the dashboard’s development. In tandem with phase 3 of co-design, we will conduct focus groups with a total of 72 stakeholders separated by disease group (cancer vs chronic kidney disease) and role (patients and care partners vs clinicians) to provide additional feedback on the appropriateness and desirability of the proposed dashboard design and content. In tandem with phase 4 of co-design, we will conduct focus groups to confirm the acceptability and usability of the dashboard (n=16-24 patients and care partners per disease group). Trained members of the research team will facilitate the discussions according to guides containing semistructured questions to follow in each session. Research staff will take notes during these sessions, which will also be audio recorded in case project staff are unable to manually document all responses.
Dartmouth model for dashboard co-design and implementation (reproduced from Coproduction Design and Implementation Flow by Van Citters [
The primary outcome of the co-design process is to build a fully functional dashboard that can be implemented for clinical testing in objective 2 of this study. However, we also anticipate the development of a rich source of quantitative and qualitative data on the dynamics of development and implementation outcomes [
Each of the outcomes will be summarized through qualitative analysis or descriptive statistics depending on the data type. For qualitative analyses, transcripts and notes from the sessions will be analyzed using a directed content analysis approach [
The second objective of this study will include testing the co-designed dashboard in the flow of clinical care service delivery. First, as a quality improvement initiative, the dashboard will be implemented at the clinic level with all eligible patients who receive care from participating clinicians. This step will allow us to collect clinic-wide quality metrics associated with dashboard implementation. Second, the project will include a longitudinal follow-up study among a subset of patients to assess changes in patient outcomes associated with using the dashboard.
Demonstration trial flow diagram.
Schedule of survey measures in demonstration triala.
Domain and subdomains | Baseline symptom and goals assessment | Subsequent symptom and goals assessments | Dashboard visit evaluation | 3-month follow-up | 6-month follow-up | |||||||
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Sociodemographic data | Age, gender, race, ethnicity, marital status, employment, and education | —b | — | — | — | ||||||
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Goals of care | De novo measure | De novo measure | — | — | — | ||||||
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Health perception | PROMISc Global01 [ |
PROMIS Global01 | — | — | — | |||||
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Global | FACT-G7d [ |
FACT-Ge GF7f item [ |
— | FACT-G7 | FACT-G7 | |||||
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Fatigue | PROMIS 2-item custom SFg [ |
PROMIS 2-item custom SF | — | PROMIS CATh [ |
PROMIS CAT | |||||
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Anxiety | PROMIS 2-item custom SF [ |
PROMIS 2-item custom SF | — | PROMIS CAT [ |
PROMIS CAT | |||||
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Pain | PROMIS 2-item custom SF [ |
PROMIS 2-item custom SF | — | PROMIS CAT [ |
PROMIS CAT | |||||
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Depression | PROMIS 2-item custom SF [ |
PROMIS 2-item custom SF | — | PROMIS CAT [ |
PROMIS CAT | |||||
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Physical function | PROMIS 2-item custom SF [ |
PROMIS 2-item custom SF | — | PROMIS CAT [ |
PROMIS CAT | |||||
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Shortness of breath | PROMIS DYSSV014 item [ |
PROMIS DYSSV014 item | — | — | — | |||||
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Urinary frequency (chronic kidney disease only) | PRO-CTCAEi 63a item [ |
PRO-CTCAE 63a item | — | — | — | |||||
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Edema | PRO-CTCAE 22b item [ |
PRO-CTCAE 22b item | — | — | — | |||||
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Nausea | PROMIS GISX49 item [ |
PROMIS GISX49 item | — | — | — | |||||
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Appetite | PROMIS GISX55 item [ |
PROMIS GISX55 item | — | — | — | |||||
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Itching (chronic kidney disease only) | PROMIS PIQSeverity04 item [ |
PROMIS PIQSeverity04 item | — | — | — | |||||
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Neuropathy (cancer only) | FACTj and GOG-NTX-4k (version 4) [ |
FACT and GOG-NTX-4 (version 4) | — | — | — | |||||
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Constipation (cancer only) | PRO-CTCAE 15a item [ |
PRO-CTCAE 15a item | — | — | — | |||||
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Diarrhea (cancer only) | PROMIS GISX38 item [ |
PROMIS GISX38 item | — | — | — | |||||
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Side effect bother | FACT-G GP5l item [ |
FACT-G GP5 item | — | — | — | |||||
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Shared decision-making | CollaboRATE [ |
— | CollaboRATE | CollaboRATE | CollaboRATE | ||||||
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Self-efficacy-managing treatments | PROMIS 4-item custom SF [ |
— | — | PROMIS 4-item custom SF | PROMIS 4-item custom SF | ||||||
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Self-efficacy-managing symptoms | PROMIS 3-item custom SF [ |
— | — | PROMIS 3-item custom SF | PROMIS 3-item custom SF | ||||||
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Treatment satisfaction | FACIT-TSm TS40 item [ |
— | FACIT-TS TS40 item | FACIT-TS TS40 item | FACIT-TS TS40 item | ||||||
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Treatment satisfaction (cancer only) | CAHPSn Cancer Care Survey [ |
— | — | CAHPS Cancer Care Survey | CAHPS Cancer Care Survey | ||||||
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Health care communication | CASEo (information factor) [ |
— | — | CASE (information factor) | CASE (information factor) | ||||||
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Medication adherence | PMASp [ |
— | — | PMAS | PMAS | ||||||
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Social isolation | PROMIS UCLAq 14x2 item [ |
— | — | PROMIS UCLA 14x2 item | PROMIS UCLA 14x2 item | ||||||
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Health literacy | SILSr [ |
— | — | SILS | SILS | ||||||
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Financial toxicity | COSTs-FACITt FT12 item [ |
— | — | COST-FACIT FT12 item | COST-FACIT FT12 item | ||||||
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Usability, acceptability, and adoption | — | — | — | SUSu [ |
SUS, SPHERE, and NoMAD | ||||||
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Fidelity of dashboard use | — | — | De novo item | — | — | ||||||
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Usability, acceptability, and adoption | — | — | — | SUS, SPHERE, and NoMAD | SUS, SPHERE, and NoMAD | ||||||
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Fidelity of dashboard use | — | — | — | De novo item | De novo item | ||||||
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Dashboard sustainability | — | — | — | — | CSATx [ |
aSingle-item names were obtained from the referenced parent measures or item banks.
bEmpty cells indicate that a given domain was not included at that particular assessment point.
cPROMIS: Patient-Reported Outcomes Measurement Information System.
dFACT-G7: Functional Assessment of Cancer Therapy-General, 7-item version.
eFACT-G: Functional Assessment of Cancer Therapy-General.
fGF7: FACT-G global quality of life item.
gSF: short form.
hCAT: computerized adaptive test.
iPRO-CTCAE: Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events.
jFACT: Functional Assessment of Cancer Therapy.
kGOG-NTX-4: Gynecologic Oncology Group-Neurotoxicity.
lFACT-G GP5: FACT-G side effect bother item.
mFACIT-TS: Functional Assessment of Chronic Illness Therapy-Treatment Satisfaction.
nCAHPS: Consumer Assessment of Health Plans Study.
oCASE: Communication and Attitudinal Self-Efficacy scale.
pPMAS: PROMIS Medication Adherence Scale.
qUCLA: University of California, Los Angeles.
rSILS: Single Item Literacy Screener.
sCOST: Comprehensive Score for Financial Toxicity.
tFACIT: Functional Assessment of Chronic Illness Therapy.
uSUS: System Usability Scale.
vSPHERE: Stroke Prevention in Healthcare Delivery Environments study.
wNoMAD: Normalization Measure Development.
xCSAT: Clinical Sustainability Assessment Tool.
The co-designed dashboard will be enabled in the health system’s electronic health record software within the clinical departments of all participating clinicians. The first month of dashboard implementation will constitute a “soft launch” period where participating clinicians and their eligible patients will be trained on using the dashboard and will work with the project team to troubleshoot and resolve any technical and practical issues. After the soft launch period, clinicians will be encouraged to use the dashboard during clinical encounters with all their patients who meet the inclusion criteria (as described previously in objective 1). As outlined in
The dashboard will display results from the patient’s most recent symptom and goals assessment and other clinical measures stored in the electronic health record. The included assessments will be determined through extensive stakeholder input during the dashboard co-design process described in objective 1. The symptom and goals assessment will include a mix of items and scales selected to optimize clinical relevance and feasibility. For instance, each assessment will include the PROMIS Global01 health perception item [
In addition to rating health-related quality of life domains, patients will respond to 5 open-response questions in the symptom and goals assessment regarding their goals of care that will populate the dashboard. These questions will prompt patients to specify (1) the top 1 or 2 concerns they would like to discuss during the visit, (2) their most concerning side effects, (3) overall goals regarding their cancer or kidney disease treatment, (4) personal goals and values, and (5) how they can work together with their care team to achieve their goals. Finally, the dashboard will display the most recent results on other clinical measures typically collected during the care process and stored within the electronic health record. For patients with cancer, the additional clinical data will include medications, weight, white blood cell count, absolute neutrophils, hemoglobin, albumin, and an appointment schedule. For patients with chronic kidney disease, the additional clinical data will include weight, blood pressure, glomerular filtration rate, hemoglobin A1c, microalbuminuria, urine protein, and an appointment schedule. Patients and clinicians will view the patient’s individualized dashboard on a computer screen during the visits. Patients will also be able to view their dashboard in between visits using their own devices (eg, computer, tablet, and mobile phone) that can access their electronic patient portal.
To assess the longitudinal changes in our primary and secondary study outcomes, a subset of approximately 200 patients will participate in a longitudinal follow-up study. Patients will be eligible to participate if they meet all other study inclusion criteria, have completed at least two dashboard-eligible visits, and provide informed consent to complete a follow-up survey at 3 and 6 months. In parallel to collecting these follow-up patient outcomes, all participating clinicians will also complete surveys at 3 and 6 months to report on their experience with the dashboard’s implementation (
For analyses focusing on health service use (see outcome description in the Health Service Use section), we will identify two cohorts of patients who were not exposed to the dashboard: (1) a cohort of eligible patients with cancer and chronic kidney disease who received care concurrently from participating Northwestern Medicine clinicians but who did not enroll in the dashboard study and (2) a matched cohort of patients with cancer and chronic kidney disease treated contemporaneously by nonparticipating Northwestern Medicine clinicians. Health service use variables will be the only study outcome data available for these 2 comparison groups.
The central outcome of the study is shared decision-making in the treatment of patients with advanced cancer or chronic kidney disease. We will use the CollaboRATE measure [
Satisfaction with health care will be assessed at baseline, 3 months, and 6 months with a single item from the Functional Assessment of Chronic Illness Therapy-Treatment Satisfaction measure [
A total of 2 subdomains of the PROMIS Self-Efficacy for Managing Chronic Conditions domain will be assessed at baseline and at the 3- and 6-month follow-ups. A 4-item custom short form created from the PROMIS Self-Efficacy for Managing Medications and Treatment Item Bank version 1.0 [
To evaluate changes in health-related quality of life as an outcome associated with using the dashboard, a subset of health-related quality of life domains included in the symptom and goals assessment will be assessed again at the 3- and 6-month follow-ups (see
After study completion, we will retrospectively extract data on the use of potentially avoidable, high-cost, or low-value health care services from the periods 6 months before and 6 months after the first dashboard use. Specific indicators will include (1) unplanned all-cause hospital admissions, (2) potentially avoidable all-cause emergency department use, (3) excess (all-cause) days in acute care within 30 days of hospital discharge, and (4) 7-day readmissions. In addition, among patients with cancer, we will assess the following disease-specific indicators: (5) admissions and emergency department visits for patients receiving outpatient chemotherapy, (6) chemotherapy within the last 14 days of life, (7) use of a triage clinic, (8) completion of an advance directive, and (9) hospice use of >3 days. Among patients with chronic kidney disease, the following additional indicators will be collected: (10) use of emergency start dialysis; (11) chronic kidney disease–related emergency department or inpatient use; and (12) progression from chronic kidney disease stage 3 to stage 4, stage 4 to stage 5, or stage 3 to stage 5.
Patient- and clinician-reported survey measures will capture relevant implementation outcomes [
For all statistical tests, a nominal 2-sided
The primary analyses for objective 2 are within-group mean changes in shared decision-making (CollaboRATE), self-efficacy for managing chronic conditions (PROMIS), satisfaction with care (Functional Assessment of Chronic Illness Therapy-Treatment Satisfaction and Consumer Assessment of Health Plans Study Cancer Care Survey), and health-related quality of life and symptoms (PROMIS, Functional Assessment of Chronic Illness Therapy, and PRO-CTCAE). First, we will test whether within-group means have changed significantly from the baseline and symptom assessments (as appropriate) to the 3-month follow-up (primary) and 6-month follow-up (secondary) using paired samples
Sample size considerations for objective 2 were informed by statistical power analyses for differences in within-group changes in mean responses to the CollaboRATE shared decision-making measure and PROMIS measures from baseline to the 3-month follow-up. Within-group mean changes in the CollaboRATE 5-point response scale version ranged from approximately 2.0 to 4.5, with SDs of approximately 3.5 [
As noted in the Methods section, all PROMIS measures are scored as a T-score metric with a mean of 50 and SD of 10. Changes as low as 3 points can be clinically meaningful [
The co-design sessions (objective 1), which focused on collaboratively designing the dashboard’s content and format for initial clinical testing, concluded in May 2020. Focus groups supplementing the co-design sessions were completed in October 2019, February 2020, and October 2021. Data collection for the demonstration trial is anticipated to be completed by the end of July 2022. Study investigators currently meet monthly as an entire team to discuss progress on manuscript development for results dissemination. Each study objective will have at least one resulting publication: (1) co-design process and outcomes and (2) demonstration trial process and outcomes.
In this study, we hypothesize that co-designing and implementing a patient-reported outcome clinical dashboard will be associated with improved processes underlying health care coproduction among adults with advanced cancer or chronic kidney disease, including shared decision-making, satisfaction with care, engagement in health care, self-efficacy in managing symptoms and treatments, health-related quality of life, and use of health services. Collaborating with patients, care partners, and clinicians to create scalable clinical dashboards can help improve coproduction outcomes [
The dashboard’s design and evaluation processes have several strengths worth noting. First, the dashboard will be co-designed by investigators and key stakeholders (patients, care partners, and clinic staff). Collaborating with stakeholders promotes person-centered care delivery and increases the likelihood that a new health care element can be successfully implemented and optimally impactful [
Despite these strengths, this project has limitations that warrant discussion. First, the study will focus only on patients in the advanced stages of 2 chronic conditions and on those who regularly access their electronic patient portals. Although advanced cancer and chronic kidney disease are two of the most common and burdensome illnesses to individuals and the health care system [
This study demonstrates how to combine stakeholder engagement, a health care coproduction framework, and health IT to develop a clinically feasible, acceptable, and scalable model of patient-centered care delivery. This study’s premise integrates emerging research indicating that better patient outcomes and appropriate health care use can be enhanced through patient-clinician collaboration in the context of well-designed workflows and systems. Thus, this study focuses on dealing with the manifold challenges of implementing significant changes in workflow and clinical approach in the real world of ambulatory care of patients with a serious chronic illness. We aim to build on implementation research to understand the needed adoption, acceptability, and feasibility of new tools to optimize a successful and sustainable transformation of the clinical encounter over time. Our work serves as an example for transforming care delivery in a way that embraces value-based care and has the potential to improve the lives of patients with chronic conditions.
Functional Assessment of Cancer Therapy-General
Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events
Patient-Reported Outcomes Measurement Information System
Research Electronic Data Capture
This publication was made possible by a grant from the Peterson Center on Healthcare. The statements made and views expressed are solely the responsibility of the authors. LMP was supported by the National Institutes of Health/National Cancer Institute training grant T32CA193193.
Although we have collected data for this study, this manuscript does not report any results. Our intention is to share data associated with the published results as they emerge.
AH is a health care consultant for the Yale New Haven Health System Center for Outcomes Research and Evaluation, where she is involved in re-evaluation activities for the Centers for Medicare and Medicaid value-based payment programs. Neither the Yale New Haven Health System Center for Outcomes Research and Evaluation nor the Centers for Medicare and Medicaid provided support or funding for this work.