Palo Alto Medical Foundation Research Institute Palo Alto
Forcino R.C.,The Dartmouth Institute for Health Policy and Clinical Practice Lebanon |
Barr P.J.,The Dartmouth Institute for Health Policy and Clinical Practice Lebanon |
O'Malley A.J.,The Dartmouth Institute for Health Policy and Clinical Practice Lebanon |
Arend R.,Dartmouth Hitchcock Patient and Family Advisory Council Lebanon |
And 7 more authors.
Health Expectations | Year: 2017
Introduction: CollaboRATE is a brief patient survey focused on shared decision making. This paper aims to (i) provide insight on facilitators and challenges to implementing a real-time patient survey and (ii) evaluate CollaboRATE scores and response rates across multiple clinical settings with varied patient populations. Method: All adult patients at three United States primary care practices were eligible to complete CollaboRATE post-visit. To inform key learnings, we aggregated all mentions of unanticipated decisions, problems and administration errors from field notes and email communications. Mixed-effects logistic regression evaluated the impact of site, clinician, patient age and patient gender on the CollaboRATE score. Results: While CollaboRATE score increased only slightly with increasing patient age (OR 1.018, 95% CI 1.014-1.021), female patient gender was associated with significantly higher CollaboRATE scores (OR 1.224, 95% CI 1.073-1.397). Clinician also predicts CollaboRATE score (random effect variance 0.146). Site-specific factors such as clinical workflow and checkout procedures play a key role in successful in-clinic implementation and are significantly related to CollaboRATE scores, with Site 3 scoring significantly higher than Site 1 (OR 1.759, 95% CI 1.216 to 2.545) or Site 2 (z=-2.71, 95% CI -1.114 to -0.178). Discussion: This study demonstrates that CollaboRATE can be used in diverse primary care settings. A clinic's workflow plays a crucial role in implementation. Patient experience measurement risks becoming a burden to both patients and administrators. Episodic use of short measurement tools could reduce this burden. © 2017 John Wiley & Sons Ltd.
Bayliss E.A.,Kaiser Permanente |
Mcquillan D.B.,Kaiser Permanente |
Ellis J.L.,Kaiser Permanente |
Maciejewski M.L.,Duke University |
And 9 more authors.
Journal of the American Geriatrics Society | Year: 2016
Objectives: To inform the development of a data-driven measure of quality care for individuals with multiple chronic conditions (MCCs) derived from an electronic health record (EHR). Design: Qualitative study using focus groups, interactive webinars, and a modified Delphi process. Setting: Research department within an integrated delivery system. Participants: The webinars and Delphi process included 17 experts in clinical geriatrics and primary care, health policy, quality assessment, health technology, and health system operations. The focus group included 10 individuals aged 70-87 with three to six chronic conditions selected from a random sample of individuals aged 65 and older with three or more chronic medical conditions. Measurements: Through webinars and the focus group, input was solicited on constructs representing high-quality care for individuals with MCCs. A working list was created of potential measures representing these constructs. Using a modified Delphi process, experts rated the importance of each possible measure and the feasibility of implementing each measure using EHR data. Results: High-priority constructs reflected processes rather than outcomes of care. High-priority constructs that were potentially feasible to measure included assessing physical function, depression screening, medication reconciliation, annual influenza vaccination, outreach after hospital admission, and documented advance directives. High-priority constructs that were less feasible to measure included goal setting and shared decision-making, identifying drug-drug interactions, assessing social support, timely communication with patients, and other aspects of good customer service. Lower-priority domains included pain assessment, continuity of care, and overuse of screening or laboratory testing. Conclusion: High-quality MCC care should be measured using meaningful process measures rather than outcomes. Although some care processes are currently extractable from electronic data, capturing others will require adapting and applying technology to encourage holistic, person-centered care. © 2016 American Geriatrics Society and Wiley Periodicals, Inc.