Enabling patient-centered care through health information technology


The main objective of the report is to review the evidence on the impact of health information technology (IT) that supports patient-centered care (PCC) on: health care processes; clinical outcomes; intermediate outcomes (patient or provider satisfaction, health knowledge and behavior, and cost); responsiveness to needs and preferences of patients; shared decisionmaking and patient-clinician communication; and access to information. Additional objectives were to identify barriers and facilitators for using health IT to deliver PCC, and to identify gaps in evidence and information needed by patients, providers, payers, and policymakers.
MEDLINE®, Embase®, Cochrane Library, Scopus, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, INSPEC, and Compendex databases through July 31, 2010.
Paired members of our team reviewed citations to identify randomized controlled trials of PCC-related health IT interventions and studies that addressed barriers and facilitators for health IT for delivery of PCC. Independent assessors rated studies for quality. Paired reviewers abstracted data.
The search identified 327 eligible articles, including 184 articles on the impact of health IT applications implemented to support PCC and 206 articles addressing barriers or facilitators for such health IT applications. Sixty-three articles addressed both questions. The study results suggested positive effects of PCC-related health IT interventions on health care process outcomes, disease-specific clinical outcomes (for diabetes mellitus, heart disease, cancer, and other health conditions), intermediate outcomes, responsiveness to the needs and preferences of patients, shared decisionmaking, patient-clinician communication, and access to medical information. Studies reported a number of barriers and facilitators for using health IT applications to enable PCC. Barriers included: lack of usability; problems with access to the health IT application due to older age, low income, education, cognitive impairment, and other factors; low computer literacy in patients and clinicians; insufficient basic formal training in health IT applications; physicians' concerns about more work; workflow issues; problems related to new system implementation, including concerns about confidentiality of patient information; depersonalization; incompatibility with current health care practices; lack of standardization; and problems with reimbursement. Facilitators for the utilization of health IT included ease of use, perceived usefulness, efficiency of use, availability of support, comfort in use, and site location.
Despite marked heterogeneity in study characteristics and quality, substantial evidence exists confirming that health IT applications with PCC-related components have a positive effect on health care outcomes. positive effect on health care outcomes.

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    ABSTRACT: There is a pressing need for greater attention to patient-centered health behavior and psychosocial issues in primary care, and for practical tools, study designs and results of clinical and policy relevance. Our goal is to design a scientifically rigorous and valid pragmatic trial to test whether primary care practices can systematically implement the collection of patient-reported information and provide patients needed advice, goal setting, and counseling in response. This manuscript reports on the iterative design of the My Own Health Report (MOHR) study, a cluster randomized delayed intervention trial. Nine pairs of diverse primary care practices will be randomized to early or delayed intervention four months later. The intervention consists of fielding the MOHR assessment -- addresses 10 domains of health behaviors and psychosocial issues -- and subsequent provision of needed counseling and support for patients presenting for wellness or chronic care. As a pragmatic participatory trial, stakeholder groups including practice partners and patients have been engaged throughout the study design to account for local resources and characteristics. Participatory tasks include identifying MOHR assessment content, refining the study design, providing input on outcomes measures, and designing the implementation workflow. Study outcomes include the intervention reach (percent of patients offered and completing the MOHR assessment), effectiveness (patients reporting being asked about topics, setting change goals, and receiving assistance in early versus delayed intervention practices), contextual factors influencing outcomes, and intervention costs. The MOHR study shows how a participatory design can be used to promote the consistent collection and use of patient-reported health behavior and psychosocial assessments in a broad range of primary care settings. While pragmatic in nature, the study design will allow valid comparisons to answer the posed research question, and findings will be broadly generalizable to a range of primary care settings. Per the pragmatic explanatory continuum indicator summary (PRECIS) framework, the study design is substantially more pragmatic than other published trials. The methods and findings should be of interest to researchers, practitioners, and policy makers attempting to make healthcare more patient-centered and relevant.Trial registration: NCT01825746.
    Implementation Science 06/2013; 8(1):73. DOI:10.1186/1748-5908-8-73 · 4.12 Impact Factor
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    ABSTRACT: Use of electronic health records (EHRs) in primary-care exam rooms changes the dynamics of patient-physician interaction. This study examines and compares doctor-patient non-verbal communication (eye-gaze patterns) during primary care encounters for three different screen/information sharing groups: (1) active information sharing, (2) passive information sharing, and (3) technology withdrawal. Researchers video recorded 100 primary-care visits and coded the direction and duration of doctor and patient gaze. Descriptive statistics compared the length of gaze patterns as a percentage of visit length. Lag sequential analysis determined whether physician eye-gaze influenced patient eye gaze, and vice versa, and examined variations across groups. Significant differences were found in duration of gaze across groups. Lag sequential analysis found significant associations between several gaze patterns. Some, such as DGP-PGD ("doctor gaze patient" followed by "patient gaze doctor") were significant for all groups. Others, such DGT-PGU ("doctor gaze technology" followed by "patient gaze unknown") were unique to one group. Some technology use styles (active information sharing) seem to create more patient engagement, while others (passive information sharing) lead to patient disengagement. Doctors can engage patients in communication by using EHRs in the visits. EHR training and design should facilitate this. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
    Patient Education and Counseling 12/2014; 98(3). DOI:10.1016/j.pec.2014.11.024 · 2.20 Impact Factor
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    ABSTRACT: We developed Therapeutic Lifestyle Change Decision Aid (TLC DA) system to support an informed choice about which behavior change to work on when multiple unhealthy behaviors are present. The system collects significant amount of information which is used to generate tailored messages to consumers in order to persuade them in following certain healthy lifestyles. One of the current limitations of the system is the necessity to collect vast amount of information from users who have to manually enter all required data. By identifying optimal set of self-reported parameters we should be able to minimize the data entry burden of the app users. The main goal of this study was to identify primary determinants of health behavior choices made by patients after using the TLC DA system. Using discriminant analysis an optimal set of predictors was identified which determined healthy behavior choices of users of a computer-mediated decision aid. We were able to reduce the initial set of 45 baseline variables to 5 primary variables driving consumer decision making regarding health behavior choice. The resulting set included smoking status, smoking cessation success estimate, self-efficacy, body mass index and diet status. Prediction of smoking cessation choice was the most accurate (73%) followed by weight management choice (67%). Physical activity and diet choices were much better identified in a combined cluster (76%–87%). The resulting minimized parameter set can significantly improve user experience.
    Big Data and Smart Computing (BigComp) 2015, Jeju, South Korea; 02/2015
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