scholarly journals Measure once, cut twice—adding patient-reported outcome measures to the electronic health record for comparative effectiveness research

2013 ◽  
Vol 66 (8) ◽  
pp. S12-S20 ◽  
Author(s):  
Albert W. Wu ◽  
Hadi Kharrazi ◽  
L. Ebony Boulware ◽  
Claire F. Snyder
Keyword(s):  
Electronic Health Record ◽  
Outcome Measures ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Health Record ◽  
Effectiveness Research ◽  
Patient Reported ◽  
Electronic Health

Implementation and early adaptation of patient-reported outcome measures into an electronic health record: A technical report

2018 ◽  
Vol 26 (1) ◽  
pp. 129-140 ◽  
Author(s):  
Heather Taffet Gold ◽  
Raj J Karia ◽  
Alissa Link ◽  
Rachel Lebwohl ◽  
Joseph D Zuckerman ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Outcome Measures ◽  
Computerized Adaptive Testing ◽  
Clinical Care ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Measurement Information ◽  
Health Record ◽  
Patient Reported ◽  
Electronic Health

We integrated and optimized patient-reported outcome measures into the electronic health record to provide quantitative, objective data regarding patients’ health status, which is important for patient care, payer contracts, and research. With a multidisciplinary team from information technology, clinical informatics, population health, and physician champions, we used formal human–computer interaction techniques and user-centered design to integrate several technology platforms and computerized adaptive testing for the National Institutes of Health Patient-Reported Outcomes Measurement Information System. The patient-reported outcome measure system leverages software frequently used by health systems and provides data for research and clinical care via a mobile-responsive web application using Symfony, with REDCap for configuring assessments and de-identified data storage. The system incorporates Oracle databases and Epic flowsheets. Patients complete patient-reported outcome measures, with data viewable in MyChart and Epic Synopsis Reports. Researchers can access data portals. The highly usable, successful patient-reported outcome measures platform is acceptable to patients and clinicians and achieved 73 percent overall completion rates.

scholarly journals Electronic health record–integrated approach for collection of patient-reported outcome measures: a retrospective evaluation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maggie E. Horn ◽  
Emily K. Reinke ◽  
Richard C. Mather ◽  
Jonathan D. O’Donnell ◽  
Steven Z. George
Keyword(s):  
Electronic Health Record ◽  
Outcome Measures ◽  
Response Rate ◽  
Response Rates ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Health Record ◽  
Preliminary Evaluation ◽  
Patient Reported ◽  
Electronic Health

Abstract Background The integration of Patient Reported Outcome Measures (PROMs) into clinical care presents many challenges for health systems. PROMs provide quantitative data regarding patient-reported health status. However, the most effective model for collecting PROMs has not been established. Therefore the purpose of this study is to report the development and preliminary evaluation of the standardized collection of PROMs within a department of orthopedic surgery at a large academic health center. Methods We utilized the Users’ Guide to Integrating Patient-Reported Outcomes in Electronic Health Records by Gensheimer et al., 2018 as a framework to describe the development of PROMs collection initiative. We framed our initiative by operationalizing the three aspects of PROM collection development: Planning, Selection, and Engagement. Next, we performed a preliminary evaluation of our initiative by assessing the response rate of patients completing PROMs (no. of PROMs completed/no. of PROMs administered) across the entire department (18 clinics), ambulatory clinics only (14 clinics), and hospital-based clinics only (4 clinics). Lastly, we reported on the mean response rates for the top 5 and bottom 5 orthopaedic providers to describe the variability across providers. Results We described the development of a fully-integrated, population health based implementation strategy leveraging the existing resources of our local EHR to maximize clinical utility of PROMs and routine collection. We collected a large volume of PROMs over a 13 month period (n = 10,951) across 18 clinical sites, 7 clinical specialties and over 100 providers. The response rates varied across the department, ranging from 29 to 42%, depending on active status for the portal to the electronic health record (MyChart). The highest single provider mean response rate was 52%, and the lowest provider rate was 13%. Rates were similar between hospital-based (26%) and ambulatory clinics (29%). Conclusions We found that our standardized PROMs collection initiative, informed by Gensheimer et al., achieved scope and scale, but faced challenges in achieving a high response rate commensurate with existing literature. However, most studies reported a targeted recruitment strategy within a narrow clinical population. Further research is needed to elucidate the trade-off between scalability and response rates in PROM collection initiatives.

scholarly journals Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research

Medical Care ◽  
2013 ◽  
Vol 51 ◽  
pp. S30-S37 ◽  
Author(s):  
William R. Hersh ◽  
Mark G. Weiner ◽  
Peter J. Embi ◽  
Judith R. Logan ◽  
Philip R.O. Payne ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Health Record ◽  
Effectiveness Research ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health
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