
==== Front
J Am Med Inform Assoc
J Am Med Inform Assoc
jamia
Journal of the American Medical Informatics Association : JAMIA
1067-5027
1527-974X
Oxford University Press

39093939
10.1093/jamia/ocae180
ocae180
Research and Applications
AcademicSubjects/MED00580
AcademicSubjects/SCI01060
AcademicSubjects/SCI01530
Shared decision-making and disease management in advanced cancer and chronic kidney disease using patient-reported outcome dashboards
https://orcid.org/0000-0002-9881-4541
Cella David PhD Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Department of Psychiatry & Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

https://orcid.org/0000-0003-3696-9086
Kuharic Maja PhD, MPharm Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Peipert John Devin PhD Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Bedjeti Katy MSW, MS Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Garcia Sofia F PhD Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Department of Psychiatry & Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

https://orcid.org/0000-0003-1503-0600
Yanez Betina PhD Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Hirschhorn Lisa R MPH, MD Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Department of Psychiatry & Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Robert J Havey, MD Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Coughlin Ava MAEd Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Morken Victoria MPH, MS, RN Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

O'Connor Mary MS Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Linder Jeffrey A MD, MPH Division of General Internal Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Jordan Neil PhD Department of Psychiatry & Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Center of Innovation for Complex Chronic Healthcare, Hines VA Hospital, Hines, IL 60141, United States

Ackermann Ronald T MPH, MD Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Division of General Internal Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Amagai Saki BA Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

https://orcid.org/0000-0002-3429-6378
Kircher Sheetal MD Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Northwestern Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Division of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Mohindra Nisha MD Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Northwestern Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Division of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Aggarwal Vikram MD Northwestern Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Division of Nephrology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Weitzel Melissa PA-C Northwestern Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Division of Nephrology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Nelson Eugene C MPH, DSc The Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States

https://orcid.org/0000-0002-0917-6286
Elwyn Glyn BA, MSc, MD, PhD The Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States

https://orcid.org/0000-0002-2961-5661
Van Citters Aricca D MS The Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States

Barnard Cynthia MBA, MSJS, PhD Division of General Internal Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
Northwestern Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States

Corresponding author: David Cella, PhD, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, 625 No. Michigan Avenue, 21st Floor, Chicago, IL 60611, United States (d-cella@northwestern.edu)
10 2024
02 8 2024
02 8 2024
31 10 21902201
09 2 2024
22 6 2024
28 6 2024
04 7 2024
14 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Objectives

To assess the use of a co-designed patient-reported outcome (PRO) clinical dashboard and estimate its impact on shared decision-making (SDM) and symptomatology in adults with advanced cancer or chronic kidney disease (CKD).

Materials and Methods

We developed a clinical PRO dashboard within the Northwestern Medicine Patient-Reported Outcomes system, enhanced through co-design involving 20 diverse constituents. Using a single-group, pretest-posttest design, we evaluated the dashboard's use among patients with advanced cancer or CKD between June 2020 and January 2022. Eligible patients had a visit with a participating clinician, completed at least two dashboard-eligible visits, and consented to follow-up surveys. PROs were collected 72 h prior to visits, including measures for chronic condition management self-efficacy, health-related quality of life (PROMIS measures), and SDM (collaboRATE). Responses were integrated into the EHR dashboard and accessible to clinicians and patients.

Results

We recruited 157 participants: 66 with advanced cancer and 91 with CKD. There were significant improvements in SDM from baseline, as assessed by collaboRATE scores. The proportion of participants reporting the highest level of SDM on every collaboRATE item increased by 15 percentage points from baseline to 3 months, and 17 points between baseline and 6-month follow-up. Additionally, there was a clinically meaningful decrease in anxiety levels over study period (T-score baseline: 53; 3-month: 52; 6-month: 50; P < .001), with a standardized response mean (SRM) of −0.38 at 6 months.

Discussion

PRO clinical dashboards, developed and shared with patients, may enhance SDM and reduce anxiety among patients with advanced cancer and CKD.

shared decision-making
clinical dashboards
PROMIS
patient-reported outcome measures
PROs
cancer
chronic kidney disease
Peterson Center on Healthcare 10.13039/100016900 #19041
==== Body
pmcIntroduction

The pursuit of optimizing person-centered care remains at the forefront of clinical practice and research.1 Person-centered care emphasizes treating or managing diseases while integrating the preferences, needs, and values of patients, enabling them to be active participants in their care decisions.2,3 Person-centered care is associated with improved patient satisfaction, engagement, adherence to treatments, and overall outcomes.4–8 However, for patients navigating complex diseases like advanced cancer or chronic kidney disease (CKD), the journey is fraught with challenges. They frequently experience varying amounts of distressing symptoms, including fatigue, pain, and emotional challenges such as anxiety or depression.9–13 These symptoms often remain unnoticed by clinicians during routine visits,14–16 resulting in persistent unmanaged symptoms, and increasing the risk of potentially avoidable healthcare resource use.17,18 Given the intricate treatment regimens, symptom burdens and emotional distress, there is an increasing emphasis on electronic tools and interventions that bridge this communication gap, by facilitating patients’ and clinicians’ collaboration on making choices about treatment plans, ie, shared decision-making (SDM).19

SDM is a collaborative process that engages patients and clinicians in making health care decisions, based on clinical evidence that balances risks and expected outcomes with patient preferences and values.20 Although SDM can strengthen the clinician-patient relationship and bring multiple perspectives into consideration when targeting a problem, it is not always readily adopted in practice.21,22 Barriers to effective SDM often revolve around communication challenges, such as discrepancies in health literacy between clinicians and patients, unmet expectations, and the inherent difficulty of conveying technical medical information.19 This is where innovative health IT tools such as patient-reported outcome (PRO)-driven clinical dashboards offer the potential to convey useful decision-making information in a manner that will be accepted by both patients and clinicians.23 PRO clinical dashboards, visible to both clinicians and patients, are interactive data visualization tools that display clinical information and longitudinal trends in health outcomes using graphs, charts, and interactive tables.24 Observational studies in individuals with chronic illnesses, suggest that the use of dashboards is associated with improved SDM. Van Citters and colleagues found that dashboards contribute to greater SDM than the use of pre-visit questionnaires alone.25 Furthermore, in another study among dashboard users, those who advocate for these tools perceive a higher level of SDM compared to detractors.26 By integrating patients' insights on their health, symptoms, and health-related quality of life (HRQoL) with electronic health record (EHR) data, PRO clinical dashboards may foster meaningful clinician-patient dialogues during and after scheduled visits, presenting both patient-generated and clinical data in a unified view, ultimately aiming to improve patient outcomes.24,27,28

Several previous studies have evaluated the use of electronic PRO reporting during cancer treatment and CKD has linked early symptom detection with clinical benefit.28–30 However, no previous study in cancer or CKD has evaluated whether clinical dashboards are associated with changes in SDM and related management outcomes in a pre-post study design. This study's main objective was to assess the effectiveness of a co-designed PRO clinical dashboard on improved SDM and disease management in adults with advanced cancer or CKD. We hypothesized that patients who used the clinical dashboard would demonstrate significant improvements from baseline to 3- and 6-month follow-ups in perceived quality of SDM, self-efficacy in managing treatments and symptoms, and HRQoL. The findings from this study have implications for enhancing person-centered care and provide valuable insights into the effectiveness of integrating PRO clinical dashboards into the EHR, underscoring the potential for transformative change in healthcare delivery and decision-making processes.

Methods

Study design

The study used a single-group, pretest-posttest study design. The protocol for this study has previously been published.31 All study procedures were approved by the Northwestern University Institutional Review Board (STU00210091, STU00211654, and STU00212634).

Participants and eligibility criteria

We collected data from patients at Northwestern Medicine, Chicago, Illinois, who had advanced cancer or CKD. The dashboard was implemented at the clinic level with all eligible patients who receive care from one or more participating clinicians. To be eligible for our study, patients must have had advanced cancer or CKD, completed at least two dashboard-eligible visits (ie visits that included a visit with the participating MD, including telehealth visits during the early COVID-19 shelter-in-place time period), and consented to follow-up surveys at both 3 and 6 months. We defined advanced cancer patients as having gastrointestinal cancer with confirmed stage 4 malignancy and had been receiving intravenous chemotherapy for at least 3 months or confirmed stage 3C or 4 lung cancer and had received first- or second-line chemotherapy for at least 3 months. CKD patients must have had a confirmed diagnosis of at least stage 3, either through clinical diagnosis or an estimated glomerular filtration rate below 60.

Co-designed PRO-driven clinical dashboards

We built the PRO clinical dashboard (Figure 1) on the foundation of the Northwestern Medicine Patient-Reported Outcomes system, which administers PRO measures electronically and integrates them into the EHR to inform clinical care delivery.32 For this study, we adapted the system to display a PRO clinical dashboard developed using a co-design process involving the collective effort of 20 engaged constituents, including investigators, patients, care partners, clinicians, and health IT professionals.31 This approach was informed by the concepts of Dartmouth’s Coproduction Design and Implementation Flow Model (CDIFM),31 which has been successful in co-designing dashboards for individuals with other chronic illnesses.25,26,33 In the first phase, the team brainstormed facilitators and barriers to SDM and optimal care planning. During the second phase, the team evaluated the current clinical workflows to address patient-reported symptoms, engage patients in completing PROs and to integrate the results into clinical care. They identified challenges, suggested solutions, and established design priorities for the dashboard. The third phase centered on building a design consensus, where collaborative dashboard mockups were developed and revised. In the fourth phase, the dashboard's usability was pilot-tested and its integration with the EHR was optimized. Parallel to these phases, we conducted several focus groups with a total of 72 constituents, identified by disease group (cancer or CKD) and role (patients, care partners, and clinicians), to gather additional feedback on the appropriateness and desirability of the proposed dashboard design and content. Trained research team members guided each session, using semi-structured questions and capturing the discussions via notes and audio recordings. This dashboard development and conceptualization process is detailed in a separate manuscript submitted for publication and under review.

Figure 1. PRO Clinical dashboards for cancer and kidney disease care. Reprinted with permission by © 2023 Epic Systems Corporation. MyChart® is a registered trademark of Epic Systems Corporation. Information includes test patient and provider data only.

Clinical dashboard evaluation and data collection

Following the PRO clinical dashboard's integration into the health system's EHR, we began a month-long soft launch to introduce the dashboard to both clinicians and eligible patients. After the soft launch, during weekly meetings with the clinical team to monitor implementation and clinical operations amidst the COVID-19 pandemic shelter-in-place orders, we encouraged clinicians to use the dashboard during clinical sessions with their patients meeting the inclusion criteria.

Approximately 72 h prior to their initial dashboard-relevant clinical visit, we sent eligible patients an electronic alert via their patient portal, prompting them to complete a baseline symptom and goals assessment. If patients did not respond, the project team assisted in completing the assessment either over the phone or directly at the clinic before the visit. Once submitted, these responses were automatically scored and incorporated into the EHR, which in turn notified the care team of any significant symptoms or needs. The dashboard, updated in real-time, displayed these trended inputs along with the patient's most recent clinical data. Clinicians utilized this information during the visit to facilitate clinician-patient communication and SDM. Separate from the clinical assessment described above, outcome data were collected directly from each participant at baseline, 3, and 6 months. Main and secondary outcomes are summarized further below.

Dashboard content

The dashboard displayed results from the patient’s most recent symptom and goals assessment and other clinical measures stored in the EHR. The co-design process informed both the content and display of specific recent clinical measures and PRO data elements collected directly from patients during the pre-visit assessment. In each pre-visit assessment, patients provided symptom assessments selected by the dashboard co-developers, and answered five open-ended questions in the symptoms and goals section about their care goals, which then populated the dashboard. These questions prompted them to specify: (1) the top one or two concerns they would like to discuss during the visit, (2) their most concerning side effects, (3) overall goals regarding their cancer or CKD treatment, (4) personal goals and values, and (5) how they could work together with their care team to achieve their goals.

Outcome measures

Shared decision-making

The collaboRATE scale is a 3-item measure used as the main outcome in our study, which assesses how patients feel that their clinicians helped them understand health issues, listen to their priorities, and incorporate these priorities into treatment decisions. It uses a 5-category response scale, ranging from 0 (“no effort was made”) to 4 (“every effort was made”).34 Scores are reported as the proportion of those endorsing the best response on all items (“top box”) and as the average numeric score across the three items. For each method, higher scores indicate better SDM.

Health care communication

The Communication and Attitudinal Self-Efficacy scale (CASE) measures patient self-efficacy in productive communication and maintaining a positive attitude during their cancer treatment.35 This 12-item instrument uses a four-point Likert scale ranging from 1 (“strongly disagree”) to 4 (“strongly agree”) and features three subscales. In our study, we focused on the “Seeking and Obtaining Information” 3-item subscale in which higher scores indicate greater self-efficacy in accessing and understanding relevant information.

Self-efficacy for managing chronic conditions

Two specific subdomains from the Patient-Reported Outcomes Measurement Information System (PROMIS) Self-Efficacy for Managing Chronic Conditions were used: (1) a 4-item custom short form derived from the PROMIS Self-Efficacy for Managing Medications and Treatment Item Bank version 1.0. was used to assess patients’ confidence in adhering to treatment and medication protocols36; (2) a 3-item custom short form, originating from the PROMIS Self-Efficacy for Managing Chronic Conditions-Managing Symptoms Item Bank version 1.0, was employed to assess patients' confidence in managing their symptoms between health care visits.36 PROMIS measures were scored as a T-score metric with a mean of 50, which represents the average level of a given domain for the U.S. general population, and the SD is 10. Changes as low as three points can be clinically meaningful.37 In this context, higher scores represent greater confidence or efficacy.

Social isolation

PROMIS—social isolation assesses the impact of patients’ social environments on their health.38 Participants responded to an item from the social isolation bank, “I feel isolated from others…” using a 5-point Likert scale (1 = Never, 5 = Always).

Financial toxicity

The summary item from the financial toxicity (COST-FACIT),39 “My illness has been a financial hardship to my family and me”, was utilized to assess the financial distress patients experience. This item was rated on a scale from 0 (“Not at all”) to 4 (“Very much”).

Health-related quality of life

Functional Assessment of Cancer Therapy-General 7-item version (FACT-G7) was used to assess changes in global HRQoL.40 Originally developed for use in oncology, it assesses multiple domains relevant to people with chronic illness, including pain, fatigue, and anxiety. Each item is scored on a 5-point Likert scale, with response options ranging from 0 (“not at all”) to 4 (“very much”). The total score is the sum of these item scores (range from 0 to 28), with higher scores indicating better HRQoL.

To optimize measurement precision using as few items as possible, we first used 2-item short forms from the PROMIS banks and then administered subsequently computerized adaptive tests at the 3- and 6-months interval covering HRQoL domains of: depression,41 fatigue,42 and physical function.43 The co-design team for cancer in addition identified anxiety41 and pain44 as relevant domains. All PROMIS measures were scored consistent with standard PROMIS measures as a T-score metric.

Data analysis

Data were reviewed prior to analyses for completeness, patterns of missingness, and distribution of responses. All available data for those with data for at least one follow-up time point were used in the analyses. Analyses were performed on the full study sample dataset and stratified by populations with CKD or cancer. Within-group mean changes in various domains were investigated using paired samples t-tests to determine if significant changes from baseline were observed at both the 3-month and 6-month follow-ups. All tests were set with a statistical significance threshold at a 2-sided P-value of <.05. The standardized response mean (SRM), a measure of effect size, was used to quantify the change in PROs over time in relation to the variability of that change. SRM was computed by dividing the mean change in scores by the standard deviation of the change. We calculated the SRM from baseline to the 3-month and 6-month follow-ups. Thresholds for interpretation were trivial (SRM < 0.20), small (SRM ≥ 0.20 < 0.50), moderate (SRM ≥ 0.50 < 0.80), or large (SRM ≥ 0.80).45 R version 4.12 was used for all analyses.

Results

Study enrollment occurred from June 2020 to January 2022. All patients seen by participating MDs during this period were invited to complete dashboard PROs. Of the 1451 eligible patients, 748 (52%) responded to PROs, constituting the quality improvement (QI) sample. Amont the QI sample, 197 (26%) consented to the follow-up study via RedCap, and 184 (25% of QI sample, 93% of those who consented) completed at least one PRO in RedCap. The analysis sample consisted of 157 participants (21% of QI sample, 80% of those consented) who provided FACT-G7 or CollaboRATE data at baseline and 3-month follow-up (Figure 2). These participants received up to five automated email reminders for each assessment, and up to four phone call outreach attempts were made before participants were considered lost to follow-up. Out of the 157 participants in the analysis sample, 66 were patients with cancer and 91 had CKD (Table 1).

Figure 2. Participant flow chart.

Table 1. Participant demographic characteristics

	Overall, N = 157	Cancer, N = 66	Kidney, N = 91		
Characteristic	N (%)	N (%)	N (%)	P-value	
Age, mean (SD)	61 (13)	61 (13)	62 (13)	.600	
Sex				.028	
 Female	79 (50.0)	40 (61.0)	39 (43.0)		
 Male	78 (50.0)	26 (39.0)	52 (57.0)		
Race/ethnicity				.013	
 Non-Hispanic White	98 (64.0)	48 (75.0)	50 (56.0)		
 Non-Hispanic Black	27 (18.0)	5 (7.8)	22 (24.0)		
 Non-Hispanic Other	22 (14.0)	10 (16.0)	12 (13.0)		
 Hispanic/Latino	7 (4.5)	1 (1.6)	6 (6.7)		
Education				.600	
 University or post-graduate degree	89 (57.0)	37 (57.0)	52 (58.0)		
 College or vocational certificate	40 (26.0)	15 (23.0)	25 (28.0)		
 High school or secondary school	22 (14.0)	12 (18.0)	10 (11.0)		
 Less than high school/secondary school	4 (2.6)	1 (1.5)	3 (3.3)		
Marital status				.016	
 Married	99 (64.0)	47 (72.0)	52 (58.0)		
 Divorced	21 (14.0)	11 (17.0)	10 (11.0)		
 Never married	19 (12.0)	2 (3.1)	17 (19.0)		
 Widowed	9 (5.8)	4 (6.2)	5 (5.6)		
 Separated	3 (1.9)	0 (0)	3 (3.4)		
 In a committed relationship	3 (1.9)	1 (1.5)	2 (2.2)		
Employment status				.200	
 Retired	65 (42.0)	25 (39.0)	40 (45.0)		
 Full-time employed	50 (33.0)	23 (36.0)	27 (30.0)		
 On disability or equivalent	16 (10.0)	3 (4.7)	13 (15.0)		
 Unemployed	8 (5.2)	4 (6.2)	4 (4.5)		
 Part-time employed	8 (5.2)	5 (7.8)	3 (3.4)		
 Homemaker	6 (3.9)	4 (6.2)	2 (2.2)		
 Full-time student only	0 (0)	0 (0)	0 (0)		
Abbreviation: SD = standard deviation.

The average age was comparable across both cohorts: 61 (SD = 13) for cancer patients and 62 (SD = 13) for those with CKD. Half of the participants (50%, N = 79) were identified as female; this included 61% of the cancer subgroup and 43% of those with CKD. Most participants (N = 98, 64%) were identified as Non-Hispanic White, including 75% in the cancer group and 56% in the kidney group. Meanwhile, Non-Hispanic Blacks made up 18% of the overall participants, with 8% in the cancer subgroup and 24% in the kidney subgroup. Hispanic/Latino participants constituted 5% of the entire group, with 2% in the cancer cohort and 7% among kidney patients.

We were able to compare the characteristics of patients who agreed to participate in the study to those who were invited but did not enroll. The analytic sample, which completed the 3-month follow-up, had a lower proportion of Hispanic or Latino participants (4.5%) compared to those who were invited but did not enroll (18%). The analytic sample also had a higher proportion of White participants (66%) and a lower proportion of Black or African American participants (18%) compared to the invited sample that did not enroll (41% White and 34% Black or African American). Additionally, the analytic sample was slightly younger, with a mean age of 61 years, compared to the invited sample that did not enroll, which had a mean age of 66 years (Table S1).

Shared decision-making

All groups showed significant collaboRATE score improvements from baseline at 3- and 6-month follow-ups (Table 2). Initially, 29% of the entire cohort reported “top box” on each item (the proportion of participants endorsing the best response on all items). By the 3-month follow-up, this percentage increased to 44% (P < .001), and at 6 months, it further rose to 46% (P < .001). In the advanced cancer subgroup, “top box” scores improved from 31% at baseline to 50% at 3 months (P = .003), and to 58% at 6 months (P = .005). For those with CKD, “top box” responses increased from 27% at baseline to 40% at 3 months (P = .029), and remained nearly steady at 39% at 6 months (P = .023).

Table 2. Change in collaboRATE, CASE, PROMIS Self-Efficacy, PROMIS Social Isolation, and COST-FACIT scores

				Baseline to 3-month	Baseline to 6-month	
Overall (N = 157)	Baseline	3-Month follow-up	6-Month follow-upa	SRM	P b	SRM	P b	
Primary								
 collaboRATE mean	3.10 (0.81)	3.39 (0.79)	3.39 (0.78)	0.32	<.001	0.40	<.001	
 collaboRATE top box [n (%)]	44 (29%)	68 (44%)	51 (46%)	–	<.001	–	<.001	
Secondary								
 CASE—seeking and obtaining information	3.79 (0.56)	3.77 (0.60)	3.83 (0.42)	−0.03	.458	0.01	.462	
 PROMIS Self-Efficacy—managing meds and treatment	51.9 (6.8)	49.2 (7.1)	51.1 (7.4)	−0.35	<.001	−0.08	.427	
 PROMIS Self Efficacy—managing symptoms	52.5 (7.7)	51.3 (7.0)	51.8 (7.3)	−0.13	.078	−0.18	.055	
 PROMIS—social isolation	1.87 (1.02)	1.91 (0.89)	1.93 (0.87)	0.06	.49	0.07	.391	
 COST—FACIT	1.03 (1.24)	0.96 (1.14)	1.04 (1.21)	−0.11	.166	−0.04	.867	
Advanced cancer (N = 66)	
Primary	
 collaboRATE mean	3.22 (0.68)	3.54 (0.60)	3.57 (0.68)	0.35	.002	0.43	0.007	
 collaboRATE top box [n (%)]	20 (31%)	32 (50%)	25 (58%)	–	.006	–	0.010	
Secondary								
 CASE—seeking and obtaining information	3.92 (0.26)	3.85 (0.51)	3.84 (0.27)	−0.09	.631	−0.14	.112	
 PROMIS Self-Efficacy—managing meds and treatment	53.1 (6.2)	49.4 (6.5)	52.0 (7.3)	−0.49	<.001	−0.21	.142	
 PROMIS Self Efficacy—managing symptoms	53.1 (7.4)	51.8 (6.7)	52.7 (8.2)	−0.13	.203	−0.14	.393	
 PROMIS—social isolation	1.88 (1.10)	1.89 (0.89)	1.93 (0.82)	0	.961	−0.1	.613	
 COST—FACIT	0.92 (1.18)	0.97 (1.16)	1.20 (1.27)	0	.888	0.16	.178	
Chronic kidney disease (N = 91)	
Primary	
 collaboRATE mean	3.01 (0.88)	3.28 (0.88)	3.27 (0.83)	0.30	.003	0.38	.010	
 collaboRATE top box [n(%)] c	24 (27%)	36 (40%)	26 (39%)	–	.045	–	.037	
Secondary								
 CASE—seeking and obtaining information	3.70 (0.69)	3.71 (0.66)	3.81 (0.50)	0.01	.65	0.12	.83	
 PROMIS Self-Efficacy—managing meds and treatment	51 (7)	49 (8)	50 (8)	−0.25	.012	0.004	.929	
 PROMIS Self Efficacy—managing symptoms	52 (8)	51 (7)	51 (7)	−0.13	.21	−0.21	.087	
 PROMIS—social isolation	1.87 (0.96)	1.92 (0.90)	1.93 (0.91)	0.1	.359	0.19	.104	
 COST—FACIT	1.10 (1.28)	0.96 (1.13)	0.92 (1.17)	−0.19	.114	−0.18	.25	
a Six-month results are based on 118 of the original 157 participants: 49 with cancer and 69 with chronic kidney disease.

b P-values are derived from McNemar’s chi-squared test for collaboRATE top box score, Wilcoxon signed-rank test for all other measures.

c “top box” scoring refers to the proportion of respondents endorsing the best response on all items.

Abbreviations: CASE = Communication and Attitudinal Self-Efficacy scale; COST—FACIT = the Comprehensive Score for Financial Toxicity: Functional Assessment of Chronic Illness Therapy; PROMIS = Patient-Reported Outcomes Measurement Information System; SD = standard deviation; SRM = standardized response mean.

When examining mean scores, the entire cohort started with an average baseline collaboRATE score of 3.10 (SD = 0.81), which increased to 3.39 (SD = 0.79, P < .001) at 3 months, accompanied by a small SRM of 0.32, and remained at 3.39 (SD = 0.78, P < .001) at 6 months with a slightly higher SRM of 0.40. Participants with advanced cancer saw their mean scores rise from 3.22 (SD = 0.68) to 3.54 (SD = 0.60, P = .002) at 3 months, with a small SRM of 0.35, and then to 3.57 (SD = 0.68, P = .007) at 6 months with an SRM of 0.43. The CKD group's mean scores increased from 3.01 (SD = 0.88) to 3.28 (SD = 0.88, P = .003, SRM = 0.30) at 3 months and to 3.27 (SD = 0.83, P = .010, SRM = 0.38) at 6 months.

For individual items, the overall group showed an improvement in SDM on all three collaboRATE items from baseline to the 3- and 6-month follow-ups (Table 3) with small to moderate SRM (0.20-0.80). In the subgroup of cancer patients, the item that measures the effort made to listen to the things that matter most to patients about their health saw the greatest improvement, with a moderate SRM of 0.51 (P = .002) from baseline to the 6-month follow-up. Conversely, in the kidney patient subgroup, the item addressing the incorporation of patients' primary concerns in subsequent treatment decisions exhibited the most notable change with a small SRM of 0.44 (P = .002) from baseline to the 6-month follow-up.

Table 3. Change in the collaboRATE single items

collaboRATE items	
	Baseline	3-Month follow-up	6-Month follow-upa	SRM (baseline to 3-month)	P b	SRM (baseline to 6-month)	P b	
Overall (N = 157)	Mean (SD)	Mean (SD)	Mean (SD)	
1. How much effort was made to help you understand your health issues?	3.14 (0.86)	3.39 (0.83)	3.33 (0.83)	0.24	<.001	0.25	.013	
2. How much effort was made to listen to the things that matter most to you about your health issues?	3.12 (0.86)	3.43 (0.77)	3.44 (0.83)	0.32	<.001	0.37	<.001	
3. How much effort was made to include what matters most to you in choosing what to do next?	3.03 (0.91)	3.35 (0.86)	3.38 (0.82)	0.33	<.001	0.41	<.001	
Advanced cancer (N = 66)	
1. How much effort was made to help you understand your health issues?	3.29 (0.76)	3.52 (0.69)	3.47 (0.79)	0.22	.041	0.22	.146	
2. How much effort was made to listen to the things that matter most to you about your health issues?	3.20 (0.75)	3.59 (0.61)	3.64 (0.65)	0.42	<.001	0.51	.002	
3. How much effort was made to include what matters most to you in choosing what to do next?	3.17 (0.80)	3.52 (0.66)	3.55 (0.76)	0.36	.002	0.38	.009	
Chronic kidney disease (N = 91)	
1. How much effort was made to help you understand your health issues?	3.02 (0.92)	3.30 (0.91)	3.24 (0.85)	0.26	.009	0.28	<.05	
2. How much effort was made to listen to the things that matter most to you about your health issues?	3.07 (0.94)	3.31 (0.85)	3.31 (0.91)	0.25	.02	0.28	.034	
3. How much effort was made to include what matters most to you in choosing what to do next?	2.92 (0.97)	3.22 (0.97)	3.27 (0.85)	0.31	.006	0.44	.002	
a Six-month results are based on 118 of the original 157 participants: 49 with cancer and 69 with kidney disease.

b P-values are derived from the Wilcoxon signed-rank test.

Abbreviations: SD = standard deviation; SRM = standardized response mean.

Health-related quality of life

In the analysis of anxiety levels, a noteworthy pattern emerged (Table 4). The cancer group showed a decrease in PROMIS Anxiety T-scores from a baseline of 53 (SD = 7) to 52 (SD = 8) at the 3-month follow-up, though this change was not statistically significant (SRM = −0.10, P = .632). By the 6-month follow-up, the score decreased to 50 (SD = 8, P = .045) with a small SRM of −0.43, which constitutes a clinically meaningful and statistically significant change from baseline. The PROMIS Anxiety measure was not administered to the CKD sample.

Table 4. Change in PROMIS T-scores and FACT-7G

Instrument	
	Baseline	3-Month follow-up	6-Month follow-upa	SRM (baseline to 3-month)	P b	SRM (baseline to 6-month)	P b	
Overall (N = 157)	Mean (SD)	Mean (SD)	Mean (SD)	
FACT G7	18.4 (5.4)	18.5 (5.0)	18.2 (5.1)	0.05	.281	−0.04	.776	
PROMIS Anxiety	53 (7)	52 (8)	50 (8)	−0.10	.632	−0.43	.045	
PROMIS Depression	48 (7)	49 (8)	48 (8)	0.16	.264	−0.04	.662	
PROMIS Fatigue	50 (10)	52 (10)	51 (9)	0.07	.436	0.06	.443	
PROMIS Pain Interference	49 (8)	50 (8)	49 (9)	0.03	.735	−0.06	.518	
PROMIS Physical Function	45 (9)	44 (9)	44 (9)	−0.10	.582	−0.27	.067	
Advanced cancer (N = 66)	
FACT G7	19.0 (4.6)	19.1 (4.5)	18.6 (4.7)	0.04	.903	−0.13	.341	
PROMIS Anxiety	53 (7)	52 (8)	50 (8)	−0.10	.632	−0.43	.045	
PROMIS Depression	48 (6)	50 (7)	48 (7)	0.37	.081	0.09	.667	
PROMIS Fatigue	49 (9)	52 (9)	50 (6)	0.16	.350	−0.08	.921	
PROMIS Pain Interference	49 (8)	50 (8)	49 (9)	−0.03	.735	−0.06	.518	
PROMIS Physical Function	47 (8)	46 (8)	46 (8)	0.02	.990	−0.31	.185	
Chronic kidney disease (N = 91)	
FACT G7	17.9 (6.0)	18.1 (5.4)	18.0 (5.4)	0.06	.234	0.02	.641	
PROMIS Anxietyc								
PROMIS Depression	48 (8)	49 (9)	48 (9)	0.01	.985	−0.11	.375	
PROMIS Fatigue	50 (10)	52 (11)	52 (11)	0.00	.788	0.14	.271	
PROMIS Pain Interferencec								
PROMIS Physical Function	44 (9)	44 (9)	43 (9)	−0.18	.492	−0.25	.182	
a Six-month results are based on 118 of the original 157 participants: 49 with cancer and 69 with chronic kidney disease.

b P-values are derived from the Wilcoxon signed-rank test.

c The co-design team for chronic kidney disease did not identify anxiety and pain as relevant domains, so these measures were not administered for the chronic kidney disease population.

Abbreviations: FACT-G7 = Functional Assessment of Cancer Therapy-General 7-item version; PROMIS = Patient-Reported Outcomes Measurement Information System; SD = standard deviation; SRM = standardized response mean.

For other measures, which include two PROMIS subscales for self-efficacy in managing treatment and symptoms, as well as HRQoL (FACT G7, PROMIS Depression, PROMIS Fatigue, PROMIS Pain Interference, PROMIS Physical Function), the observed changes from baseline to 3- and 6-month follow-ups were not statistically significant, and both overall and across disease groups effect sizes were mostly trivial (SRM < 0.20).

Discussion

We examined the impact of a co-designed PRO clinical dashboard on enhancing SDM and related outcomes among adults with advanced cancer and CKD. We found, as hypothesized, that patients with advanced cancer and CKD experienced significant improvements in SDM from baseline to both 3- and 6-month follow-ups. Cancer patients reported reduced anxiety and an enhanced experience of being listened to about their primary health concerns. CKD patients reported a notable increase in the consideration given to their preferences when deciding subsequent treatment actions. As this was a single-group longitudinal study, we cannot be certain that the changes seen over time are attributable to the dashboard intervention itself. Parallel factors such as close follow-up, increased attention and interest shown by clinicians in SDM, motivated patient selection bias, and desire to please providers could also have a salutary effect on SDM and anxiety.

This trend resonates with the emphasis on patient engagement tools in the literature, suggesting that when people are offered real-time insights into their health data, they feel more in control and better equipped to engage in meaningful conversations with clinicians.46 Notably, the most significant improvement in HRQoL domains was a reduction in anxiety (assessed solely in cancer sample). This highlights a mental well-being benefit derived from actively engaging patients in a collaborative approach where healthcare professionals and patients work together to co-produce informed decisions. Other studies have similarly underscored the positive impact of SDM. A recent study focusing on patients with atrial fibrillation illustrated that understanding of pros and cons of oral anticoagulant treatment through SDM substantially reduced anxiety related to therapeutic decisions and increased willingness to accept treatment options.47 Integrating well designed tools like the PRO clinical dashboard can bolster SDM, concurrently improving patients' mental well-being and healthcare experience.

Among patients with cancer, anxiety frequently emerges as a natural response to uncertainty, suffering, and the possibility of mortality.48 In a systematic review of the research literature concerning patients undergoing cancer treatment, authors found that 36%-42% of patients reported feelings such as worry and nervousness.49 This aligns with the National Cancer Institute report found that 44% of patients with cancer reported some anxiety, and 23% reported significant anxiety.50 In addition, anxiety disorder that meets diagnostic criteria affects about 10% of cancer patients regardless of cancer type, disease stage, or treatment.51,52 Clinical consequences of anxiety are manifold, including less effective medical decision making,53 exacerbation of medical symptoms,53 and disruptions in cancer care.48,53 Due to anxiety’s prevalence and clinical relevance, both psychosocial and pharmacological interventions have been attempted, and both have shown evidence of effectiveness for reducing anxiety among cancer patients, although psychosocial interventions have had more modest effects.48 There is emerging but limited data suggesting that interventions to enhance SDM can reduce anxiety symptoms.54 Grote et al55 noted significantly reduced anxiety as an outcome of enhanced standard intensive maternity support with a multi-component intervention (MOMCare) including efforts to achieve SDM for depression management.55 While it is possible that the dashboard in the current study helped reduce anxiety by enhancing SDM among cancer patients, additional research will be needed to confirm the benefit and identify mechanisms for this effect.

This study has several implications for clinical practice and future research. The results from our study provide support for conducting a confirming comparative clinical trial. Also, although we observed a significant increase in collaboRATE “top box” scores, still many of our patients did not provide top box responses, reflecting need for continued improvement. We observed a lower proportion of top box collaboRATE scores compared to a study of inflammatory bowel disease patients who reported over 80% top scores in patients using the dashboard.25 Nevertheless, the difference between the group that used the dashboard and control groups was consistent at 17% in IBD, mirroring the increase we observed in our pre-post study design. These differences between studies may stem from various factors, including differences in patient populations, disease severity, or variations in clinical workflows and dashboard implementations. Further investigation is necessary to understand the underlying reasons for this variation and to identify opportunities for enhancing patient-clinician communication and decision-making in our settings.

Despite the observed improvements in SDM outcomes, there remains clear room for further enhancement. Our experience while doing this work was that the tremendous enthusiasm and energy observed in the co-design teams that built the dashboards, was not necessarily met with comparable enthusiasm among clinicians and patients who did not participate in the co-design. A recent systematic review by Glenwright et al emphasized the importance of engaging both patients and clinicians early in the design and implementation of ePRO to optimize their relevance, usability, and sustainability.56 Related studies, on PRO dashboards in telemedicine, echo the challenges of competing priorities and time constraints and suggest strategies like demonstrating positive impact and providing patient education.57 Ongoing research aims to provide a comprehensive understanding of multi-level implementation determinants.58 Engaging clinicians and patients not directly involved in the development process, through education, feedback, and iterative refinements, could enhance the adoption and sustainability of PRO dashboards. Also, it will be important to address the reality that some patients struggle to understand how to interpret or work with visual displays as they currently exist in EHRs, creating inequity in benefit derived from their use. This is likely to affect patients with lower educational level disproportionally. Deploying more easily-interpreted displays will likely help offset this.

With the growing emphasis on person-centered care, a PRO dashboard may be a key mechanism for aligning treatment approaches with patient priorities and fostering meaningful discussion. Health care organizations might aim to integrate PRO clinical dashboards into routine patient care, given their effectiveness in promoting SDM and reducing patient anxiety. These results, especially the evident decrease in anxiety levels, hold substantial promise for improving the psychological well-being of patients. Future investigations should explore the utility of clinical dashboards in other conditions and implementation in other healthcare systems, evaluating their scalability and sustainability. To ensure comprehensive person-centered care, it is important to recognize the limited impact observed in other dimensions of HRQoL. This presents avenues for future studies to further examine how clinical data align with specific HRQoL domains. Adapting the dashboard to diverse linguistic and medical contexts is another future priority, with potential for a comprehensive randomized controlled trial.

Despite its strengths, this study has several limitations. It was done at a single healthcare institution and focused on patients in advanced stages of two chronic conditions, limiting our ability to generalize the findings to other settings and conditions. It would be beneficial to expand the PRO clinical dashboard research to other conditions in future studies. The study used a single-group pretest-posttest design, limiting our ability to make causal inferences about PRO changes. Demographic differences between those who were included in the analysis sample and those who were not introduce the potential for selection bias. Our analytic sample had a lower representation of Hispanic or Latino and Black or African American participants, a higher representation of White participants, and a slightly younger mean age. These disparities align with known challenges in PRO collection among certain racial and ethnic groups and older adults.59 Further efforts are needed to ensure more representative participation in PRO research and to address barriers to enrollment and retention among underrepresented populations. Furthermore, we did not have a systematic way to track whether and how patients interacted with the dashboard during their clinical encounters and there is currently no marker in the EHR to determine if clinicians viewed the dashboard prior to or during the visit. The observed effects on SDM and anxiety could theoretically have occurred without patients directly engaging with the dashboard content. To strengthen future studies, we recommend incorporating objective usage metrics that can capture both clinician and patient interaction with dashboard during clinical encounters (eg EHR login, direct observation or patient self-report measures of dashboard engagement). This limitation underscores the need for enhanced EHR functionalities to better evaluate the real-time utility and impact of such digital health tools in clinical practice. Additionally, the English-only and digital format of our clinical dashboard could have excluded some groups, such as individuals less familiar with technology, lacking access to internet resources, and possibly lower health literacy, therefore limiting generalizability. Finally, while the EHR software in which the dashboard was developed was effective in reducing clinical burden and integrating PROs with other data, it also has some limitations in how data are visualized, which might limit opportunities to implement some desirable features revealed by user-centered design.

Conclusion

This study underscores the potential of incorporating pre-visit assessments of patient-reported symptoms and treatment goals into PRO clinical dashboards to improve SDM for patients with advanced cancer and CKD. The observed decrease in patient anxiety levels in the cancer group highlights the dashboard’s potential as a valuable tool to enhance person-centered care. Leveraging real-time patient feedback and data analytics, PRO clinical dashboards offer an opportunity to align treatment approaches with patient priorities. As we advance further into the digital age of healthcare, broader application of EHR tools such dashboards could be instrumental in working toward the incorporation of patient SDM into clinical workflows and comprehensive care plans and supports, in ways that enable a more personalized and effective patient care experience.

Supplementary Material

ocae180_Supplementary_Data

Acknowledgments

We gratefully acknowledge the financial support provided by the Peterson Center on Healthcare (PI: Cella). Also, we are grateful to the patients and care partners who contributed to the dashboard's design and to those patients who participated in its testing. Their insights and expertise were instrumental to our research.

Author contributions

David Cella, Sofia F. Garcia, Betina Yanez, Lisa R. Hirschhorn, Ronald T. Ackermann, Eugene C. Nelson, Glyn Elwyn, Aricca D. Van Citters (Conceptualization), Katy Bedjeti, Ava Coughlin, Mary O'Connor (Data curation), Katy Bedjeti, Devin Peipert (Formal analysis), David Cella (Funding acquisition), David Cella, Devin Peipert, Sofia F. Garcia, Betina Yanez, Lisa R. Hirschhorn, Neil Jordan, Ronald T. Ackermann, Eugene C. Nelson, Glyn Elwyn, Aricca D. Van Citters (Methodology), David Cella, Maja Kuharic, Devin Peipert, Katy Bedjeti, Sofia F. Garcia, Betina Yanez, Lisa R. Hirschhorn, Ava Coughlin, Victoria Morken, Mary O'Connor, Jeffrey A. Linder, Neil Jordan, Ronald T. Ackermann, Sheetal Kircher, Nisha Mohindra, Eugene C. Nelson, Glyn Elwyn, Aricca D. Van Citters, Cynthia Barnard, Saki Amagai, Vikram Aggarwal, Melissa Weitzel (Interpretation of data), David Cella (Supervision), David Cella, Maja Kuharic, Devin Peipert (Writing—original draft), David Cella, Maja Kuharic, Devin Peipert, Katy Bedjeti, Sofia F. Garcia, Betina Yanez, Lisa R. Hirschhorn, Ava Coughlin, Victoria Morken, Mary O'Connor, Jeffrey A. Linder, Neil Jordan, Ronald T. Ackermann, Sheetal Kircher, Nisha Mohindra, Eugene C. Nelson, Glyn Elwyn, Aricca D. Van Citters, Cynthia Barnard, Saki Amagai, Vikram Aggarwal, Melissa Weitzel (Writing—review & editing). Final approval of the version to be published, agreement to be accountable for all aspects of the work: all authors.

Supplementary material

Supplementary material is available at Journal of the American Medical Informatics Association online.

Funding

This work was supported by Peterson Center on Healthcare [grant number #19041 (PI: D.C.)].

Conflicts of interest

None declared.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.
==== Refs
References

1 Berwick DM , NolanTW, WhittingtonJ.  The triple aim: care, health, and cost. Health Aff (Millwood). 2008;27 (3 ):759-769. 10.1377/hlthaff.27.3.759 18474969
2 Epstein RM , StreetRLJr. The values and value of patient-centered care. Ann Fam Med. 2011;9 (2 ):100-103. 10.1370/afm.1239 21403134
3 Barry MJ , Edgman-LevitanS.  Shared decision making—pinnacle of patient-centered care. N Engl J Med. 2012;366 (9 ):780-781. 10.1056/NEJMp1109283 22375967
4 Rathert C , WyrwichMD, BorenSA.  Patient-centered care and outcomes: a systematic review of the literature. Med Care Res Rev. 2013;70 (4 ):351-379. 10.1177/1077558712465774 23169897
5 Lie HC , JuvetLK, StreetRLJr., et al  Effects of physicians' information giving on patient outcomes: a systematic review. J Gen Intern Med. 2022;37 (3 ):651-663. 10.1007/s11606-021-07044-5 34355348
6 Biglu MH , NateqF, GhojazadehM, AsgharzadehA.  Communication skills of physicians and patients' satisfaction. Mater Sociomed. 2017;29 (3 ):192-195. 10.5455/msm.2017.29.192-195 29109665
7 Zolnierek KB , DimatteoMR.  Physician communication and patient adherence to treatment: a meta-analysis. Med Care. 2009;47 (8 ):826-834. 10.1097/MLR.0b013e31819a5acc 19584762
8 Stewart MA.  Effective physician-patient communication and health outcomes: a review. CMAJ. 1995;152 (9 ):1423-1433.7728691
9 Jhamb M , Abdel-KaderK, YabesJ, et al  Comparison of fatigue, pain, and depression in patients with advanced kidney disease and cancer-symptom burden and clusters. J Pain Symptom Manage. 2019;57 (3 ):566-575.e3. 10.1016/j.jpainsymman.2018.12.006 30552961
10 Dhingra L , BarrettM, KnotkovaH, et al  Symptom distress among diverse patients referred for community-based palliative care: sociodemographic and medical correlates. J Pain Symptom Manage. 2018;55 (2 ):290-296. 10.1016/j.jpainsymman.2017.08.015 28844624
11 Henchoz Y , BülaC, GuessousI, et al  Chronic symptoms in a representative sample of community-dwelling older people: a cross-sectional study in Switzerland. BMJ Open. 2017;7 (1 ):e014485. 10.1136/bmjopen-2016-014485
12 Goh ZS , GrivaK.  Anxiety and depression in patients with end-stage renal disease: impact and management challenges—a narrative review. Int J Nephrol Renovasc Dis. 2018;11 :93-102. 10.2147/ijnrd.S126615 29559806
13 Curran L , SharpeL, ButowP.  Anxiety in the context of cancer: a systematic review and development of an integrated model. Clin Psychol Rev. 2017;56 :40-54. 10.1016/j.cpr.2017.06.003 28686905
14 Pearman T , GarciaS, PenedoF, YanezB, WagnerL, CellaD.  Implementation of distress screening in an oncology setting. J Community Support Oncol. 2015;13 (12 ):423-428. 10.12788/jcso.0198 26863872
15 Laugsand EA , SprangersMA, BjordalK, SkorpenF, KaasaS, KlepstadP.  Health care providers underestimate symptom intensities of cancer patients: a multicenter European study. Health Qual Life Outcomes. 2010;8 :104. 10.1186/1477-7525-8-104 20858248
16 Basch E , IasonosA, McDonoughT, et al  Patient versus clinician symptom reporting using the National Cancer Institute Common Terminology Criteria for Adverse Events: results of a questionnaire-based study. Lancet Oncol. 2006;7 (11 ):903-909. 10.1016/s1470-2045(06)70910-x 17081915
17 Hsia RY , NiedzwieckiM.  Avoidable emergency department visits: a starting point. Int J Qual Health Care. 2017;29 (5 ):642-645. 10.1093/intqhc/mzx081 28992158
18 Giannouchos TV , KumHC, FosterMJ, OhsfeldtRL.  Characteristics and predictors of adult frequent emergency department users in the United States: a systematic literature review. J Eval Clin Pract. 2019;25 (3 ):420-433. 10.1111/jep.13137 31044484
19 Elwyn G , FroschD, ThomsonR, et al  Shared decision making: a model for clinical practice. J Gen Intern Med. 2012;27 (10 ):1361-1367. 10.1007/s11606-012-2077-6 22618581
20 Elwyn G , EdwardsA, KinnersleyP, GrolR.  Shared decision making and the concept of equipoise: the competences of involving patients in healthcare choices. Br J Gen Pract. 2000;50 (460 ):892-899.11141876
21 Waddell A , LennoxA, SpassovaG, BraggeP.  Barriers and facilitators to shared decision-making in hospitals from policy to practice: a systematic review. Implement Sci. 2021;16 (1 ):74. 10.1186/s13012-021-01142-y 34332601
22 Légaré F , RattéS, StaceyD, et al  Interventions for improving the adoption of shared decision making by healthcare professionals. Cochrane Database Syst Rev. 2010;(5 ):Cd006732. 10.1002/14651858.CD006732.pub2 20464744
23 Bach K , MarlingC, MorkPJ, AamodtA, MairFS, NichollBI.  Design of a clinician dashboard to facilitate co-decision making in the management of non-specific low back pain. J Intell Inf Syst. 2019;52 (2 ):269-284. 10.1007/s10844-018-0539-y
24 Dowding D , RandellR, GardnerP, et al  Dashboards for improving patient care: review of the literature. Int J Med Inform. 2015;84 (2 ):87-100. 10.1016/j.ijmedinf.2014.10.001 25453274
25 Van Citters AD , HolthoffMM, KennedyAM, et al  Point-of-care dashboards promote coproduction of healthcare services for patients with inflammatory bowel disease. Int J Qual Health Care. 2021;33 (Supplement_2 ):ii40-ii47. 10.1093/intqhc/mzab067 34849970
26 Van Citters AD , TaxterAJ, MathewSD, et al  Enhancing care partnerships using a rheumatology dashboard: bringing together what matters most to both patients and clinicians. ACR Open Rheumatol. 2023;5 (4 ):190-200. 10.1002/acr2.11533 36852527
27 Snyder CF , AaronsonNK, ChoucairAK, et al  Implementing patient-reported outcomes assessment in clinical practice: a review of the options and considerations. Qual Life Res. 2012;21 (8 ):1305-1314. 10.1007/s11136-011-0054-x 22048932
28 van der Horst DEM , van Uden-KraanCF, ParentE, et al  Optimizing the use of patients' individual outcome information—development and usability tests of a chronic kidney disease dashboard. Int J Med Inform. 2022;166 :104838. 10.1016/j.ijmedinf.2022.104838 35940044
29 Basch E , DealAM, KrisMG, et al  Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34 (6 ):557-565. 10.1200/jco.2015.63.0830 26644527
30 Nipp RD , El-JawahriA, RuddyM, et al  Pilot randomized trial of an electronic symptom monitoring intervention for hospitalized patients with cancer. Ann Oncol. 2019;30 (2 ):274-280. 10.1093/annonc/mdy488 30395144
31 Perry LM , MorkenV, PeipertJD, et al  Patient-reported outcome dashboards within the electronic health record to support shared decision-making: protocol for co-design and clinical evaluation with patients with advanced cancer and chronic kidney disease. JMIR Res Protoc. 2022;11 (9 ):e38461. 10.2196/38461 36129747
32 Garcia SF , WortmanK, CellaD, et al  Implementing electronic health record-integrated screening of patient-reported symptoms and supportive care needs in a comprehensive cancer center. Cancer. 2019;125 (22 ):4059-4068. 10.1002/cncr.32172 31373682
33 Van Citters AD , GiffordAH, BradyC, et al  Formative evaluation of a dashboard to support coproduction of healthcare services in cystic fibrosis. J Cyst Fibros. 2020;19 (5 ):768-776. 10.1016/j.jcf.2020.03.009 32354650
34 Elwyn G , BarrPJ, GrandeSW, ThompsonR, WalshT, OzanneEM.  Developing collaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ Couns. 2013;93 (1 ):102-107. 10.1016/j.pec.2013.05.009 23768763
35 Wolf MS , ChangCH, DavisT, MakoulG.  Development and validation of the Communication and Attitudinal Self-Efficacy scale for cancer (CASE-cancer). Patient Educ Couns. 2005;57 (3 ):333-341. 10.1016/j.pec.2004.09.005 15893217
36 Gruber-Baldini AL , VelozoC, RomeroS, ShulmanLM.  Validation of the PROMIS® measures of self-efficacy for managing chronic conditions. Qual Life Res. 2017;26 (7 ):1915-1924. 10.1007/s11136-017-1527-3 28239781
37 Terwee CB , PeipertJD, ChapmanR, et al  Minimal important change (MIC): a conceptual clarification and systematic review of MIC estimates of PROMIS measures. Qual Life Res. 2021;30 (10 ):2729-2754. 10.1007/s11136-021-02925-y 34247326
38 Hahn EA , DeWaltDA, BodeRK, et al  New English and Spanish social health measures will facilitate evaluating health determinants. Health Psychol. 2014;33 (5 ):490-499. 10.1037/hea0000055 24447188
39 de Souza JA , YapBJ, HlubockyFJ, et al  The development of a financial toxicity patient-reported outcome in cancer: the COST measure. Cancer. 2014;120 (20 ):3245-3253. 10.1002/cncr.28814 24954526
40 Yanez B , PearmanT, LisCG, BeaumontJL, CellaD.  The FACT-G7: a rapid version of the functional assessment of cancer therapy-general (FACT-G) for monitoring symptoms and concerns in oncology practice and research. Ann Oncol. 2013;24 (4 ):1073-1078. 10.1093/annonc/mds539 23136235
41 Pilkonis PA , ChoiSW, ReiseSP, StoverAM, RileyWT, CellaD.  Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): depression, anxiety, and anger. Assessment. 2011;18 (3 ):263-283. 10.1177/1073191111411667 21697139
42 Lai JS , CellaD, ChoiS, et al  How item banks and their application can influence measurement practice in rehabilitation medicine: a PROMIS fatigue item bank example. Arch Phys Med Rehabil. 2011;92 (10 Suppl ):S20-S27. 10.1016/j.apmr.2010.08.033 21958919
43 Schalet BD , KaatA, VrahasM, BuckenmaierCTIII, BarnhillR, GershonRC.  Extending the ceiling of an item bank: development of above-average physical function items for PROMIS. In: Quality if Life Research. Springer; 2016:109.
44 Amtmann D , CookKF, JensenMP, et al  Development of a PROMIS item bank to measure pain interference. Pain. 2010;150 (1 ):173-182. 10.1016/j.pain.2010.04.025 20554116
45 Cohen J.  Statistical Power Analysis for the Behavioral Sciences. Academic Press; 2013.
46 Chen J , OuL, HollisSJ.  A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organisations in an oncologic setting. BMC Health Serv Res. 2013;13 (1 ):211. 10.1186/1472-6963-13-211 23758898
47 Chiu H-H , ChangS-L, ChengH-M, et al  Shared decision making for anticoagulation reduces anxiety and improves adherence in patients with atrial fibrillation. BMC Med Inform Decis Mak. 2023;23 (1 ):163. 10.1186/s12911-023-02260-x 37608374
48 Traeger L , GreerJA, Fernandez-RoblesC, TemelJS, PirlWF.  Evidence-based treatment of anxiety in patients with cancer. J Clin Oncol. 2012;30 (11 ):1197-1205. 10.1200/jco.2011.39.5632 22412135
49 Reilly CM , BrunerDW, MitchellSA, et al  A literature synthesis of symptom prevalence and severity in persons receiving active cancer treatment. Support Care Cancer. 2013;21 (6 ):1525-1550. 10.1007/s00520-012-1688-0 23314601
50 PDQ Supportive and Palliative Care Editorial Board. Adjustment to cancer: anxiety and distress (PDQ®): health professional version. PDQ Cancer Information Summaries. National Cancer Institute; 2002.
51 Pitman A , SulemanS, HydeN, HodgkissA.  Depression and anxiety in patients with cancer. BMJ. 2018;361 :k1415. 10.1136/bmj.k1415 29695476
52 Mitchell AJ , ChanM, BhattiH, et al  Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: a meta-analysis of 94 interview-based studies. Lancet Oncol. 2011;12 (2 ):160-174. 10.1016/S1470-2045(11)70002-X 21251875
53 Latini DM , HartSL, KnightSJ, et al  The relationship between anxiety and time to treatment for patients with prostate cancer on surveillance. J Urol. 2007;178 (3 Pt 1 ):826-832. 10.1016/j.juro.2007.05.039 17632144
54 Marshall T , StellickC, Abba-AjiA, et al  The impact of shared decision-making on the treatment of anxiety and depressive disorders: systematic review. BJPsych Open. 2021;7 (6 ):e189. 10.1192/bjo.2021.1028
55 Grote NK , KatonWJ, RussoJE, et al  Collaborative care for perinatal depression in socioeconomically disadvantaged women: a randomized trial. Depress Anxiety. 2015;32 (11 ):821-834. 10.1002/da.22405 26345179
56 Glenwright BG , SimmichJ, CottrellM, et al  Facilitators and barriers to implementing electronic patient-reported outcome and experience measures in a health care setting: a systematic review. J Patient Rep Outcomes. 2023;7 (1 ):13. 10.1186/s41687-023-00554-2 36786914
57 Mohindra NA , CoughlinA, KircherS, et al  Implementing a patient-reported outcome dashboard in oncology telemedicine encounters: clinician and patient adoption and acceptability. JCO Oncol Pract. 2024;20 (3 ):409-418. 10.1200/OP.23.00493 38207229
58 Fontaine G , PoitrasME, SassevilleM, et al  Barriers and enablers to the implementation of patient-reported outcome and experience measures (PROMs/PREMs): protocol for an umbrella review. Syst Rev. 2024;13 (1 ):96. 10.1186/s13643-024-02512-5 38532492
59 Sykes LL , WalkerRL, NgwakongnwiE, QuanH.  A systematic literature review on response rates across racial and ethnic populations. Can J Public Health. 2010;101 (3 ):213-219. 10.1007/BF03404376 20737812
