Implementing patient-reported outcome surveys as part of routine care: lessons from an academic radiation oncology department

Rotenstein, Lisa S; Agarwal, Ankit; O’Neil, Kelly; Kelly, Andrea; Keaty, Maureen; Whitehouse, Colleen; Kalinowski, B; Orio, Peter F.; Wagle, Neil W.; Martin, Neil E.

doi:10.1093/jamia/ocx009

Cited by 47 publications

(83 citation statements)

References 19 publications

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“…Consistent with previous inquiry, engagement with physicians, nurses and administrators, that have an interest in using the tools and serve as champions within their own practices, has proven successful in each of the areas we have implemented the ePROMs 16. In particular, front-line staff perceptions of ePROMs usability are critical to implementation, as they are providing the iPad to the patient, explaining the process and are responsible for ensuring that patients complete questionnaires.…”

Section: Lessons Learntmentioning

confidence: 58%

Implementing electronic patient-reported outcomes measurements: challenges and success factors

et al. 2018

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Section: Lessons Learntmentioning

confidence: 58%

Implementing electronic patient-reported outcomes measurements: challenges and success factors

et al. 2018

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“…Previous studies that attempt to integrate PROs into clinical workflow for cancer and orthopedic care have centered on in-clinic patient reports or have used paper workarounds. [39][40][41] We hope that EHR designers and implementers will consider this need as a future design requirement and create a standardized location for these types of data, perhaps as part of an overview or agenda functionality.…”

Section: Discussionmentioning

confidence: 99%

Core Components for a Clinically Integrated mHealth App for Asthma Symptom Monitoring

et al. 2017

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Background mHealth apps may be useful tools for supporting chronic disease management. Objective Our aim was to apply user-centered design principles to efficiently identify core components for an mHealth-based asthma symptom–monitoring intervention using patient-reported outcomes (PROs). Methods We iteratively combined principles of qualitative research, user-centered design, and “gamification” to understand patients' and providers' needs, develop and refine intervention components, develop prototypes, and create a usable mobile app to integrate with clinical workflows. We identified anticipated benefits and burdens for stakeholders. Results We conducted 19 individual design sessions with nine adult patients and seven clinicians from an academic medical center (some were included multiple times). We identified four core intervention components: (1) Invitation—patients are invited by their physicians. (2) Symptom checks—patients receive weekly five-item questionnaires via the app with 48 hours to respond. Depending on symptoms, patients may be given the option to request a call from a nurse or receive one automatically. (3) Patient review—in the app, patients can view their self-reported data graphically. (4) In-person visit—physicians have access to patient-reported symptoms in the electronic health record (EHR) where they can review them before in-person visits. As there is currently no location in the EHR where physicians would consistently notice these data, recording a recent note was the best option. Benefits to patients may include helping decide when to call their provider and facilitating shared decision making. Benefits to providers may include saving time discussing symptoms. Provider organizations may need to pay nurses extra, but those costs may be offset by reduced visits and hospitalizations. Conclusion Recent systematic reviews show inconsistent outcomes and little insight into functionalities required for mHealth asthma interventions, highlighting the need for systematic intervention design. We identified specific features for adoption and engagement that meet the stated needs of users for asthma symptom monitoring.

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“…As the IOM noted in 2013, 221 “Originally designed for billing and coding purposes, health IT systems have not been integrated efficiently into clinical care, do not facilitate the coordination of care, and the need to customize local systems has created a situation where health IT systems cannot communicate with each other… Many of these systems are inflexible and thus are unable to adapt to the changing needs of a modern health care system.” When collected as part of clinical care, PGHD may be buried in the clinical record, with poor visibility and interpretability. For example, Rotenstein et al 222 reported barriers to the use of PRO data in a large radiation oncology clinic, including difficulty accessing data in the EMR (60%), difficulty bringing the data into the treatment note (48%), too much data (38%), and difficulty interpreting data (26%) 222 . Notably, these barriers also apply to remotely collected biometric data and could be addressed through better EMR integration and visualization.…”

Section: Challenges and Opportunities For Clinical Integration Of Pghdmentioning

confidence: 99%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

113

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Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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Implementing patient-reported outcome surveys as part of routine care: lessons from an academic radiation oncology department

Cited by 47 publications

References 19 publications

Implementing electronic patient-reported outcomes measurements: challenges and success factors

Implementing electronic patient-reported outcomes measurements: challenges and success factors

Core Components for a Clinically Integrated mHealth App for Asthma Symptom Monitoring

Innovations in research and clinical care using patient‐generated health data

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