Assessment of a Mobile Health iPhone App for Semiautomated Self-management of Chronic Recurrent Medical Conditions Using an N-of-1 Trial Framework: Feasibility Pilot Study

BACKGROUND

Management of chronic recurrent medical conditions (CRMCs), such as migraine headaches, chronic pain, and anxiety/depression, remains a major challenge for modern providers. Our team has developed an edge-based, semiautomated mobile health (mHealth) technology called iMTracker that employs the N-of-1 trial approach to allow self-management of CRMCs.

OBJECTIVE

This study examines the patterns of adoption, identifies CRMCs that users selected for self-application, and explores barriers to use of the iMTracker app.

METHODS

This is a feasibility pilot study with internet-based recruitment that ran from May 15, 2019, to December 23, 2020. We recruited 180 patients to pilot test the iMTracker app for user-selected CRMCs for a 3-month period. Patients were administered surveys before and after the study.

RESULTS

We found reasonable usage rates: a total of 73/103 (70.9%) patients who were not lost to follow-up reported the full 3-month use of the app. Most users chose to use the iMTracker app to self-manage chronic pain (other than headaches; 80/212, 37.7%), followed by headaches in 36/212 (17.0%) and mental health (anxiety and depression) in 27/212 (12.8%). The recurrence rate of CRMCs was at least weekly in over 93% (169/180) of patients, with 36.1% (65/180) of CRMCs recurring multiple times in a day, 41.7% (75/180) daily, and 16.1% (29/180) weekly. We found that the main barriers to use were the design and technical function of the app, but that use of the app resulted in an improvement in confidence in the efficiency and safety/privacy of this approach.

CONCLUSIONS

The iMTracker app provides a feasible platform for the N-of-1 trial approach to self-management of CRMCs, although internet-based recruitment provided limited follow-up, suggesting that in-person evaluation may be needed. The rate of CRMC recurrence was high enough to allow the N-of-1 trial assessment for most traits.

Fast-Tracking Health Data Standards Development and Adoption in Real-World Settings: A Pilot Approach

BACKGROUND

Pilot-testing is important in standards development because it facilitates agile navigation of the gap between needs for and use of standards in real-world settings and can reveal the practicalities of implementation. As the implementation and use of health data standards are usually more complicated than anticipated, the Office of the National Coordinator for Health Information Technology (ONC) routinely oversees and organizes relevant pilot projects.

OBJECTIVES

This article provides an in-depth look into a sample of ONC's standards-focused pilot projects to (1) inform readers of the complexities of developing, implementing, and advancing standards and (2) guide those seeking to evaluate new standards through pilot projects.

METHODS

The ONC's approach to conducting pilot projects begins with identifying a clinical care need, research requirement, or policy outcome that is not well supported by existing standards through a landscape review. ONC then selects a testing approach based on the identified need and maturity of relevant standards. Next, ONC identifies use cases and sites to pilot-test the relevant standard. Once complete, ONC publishes a report that informs subsequent projects and standards development.

RESULTS

Pilot projects presented here are organized into three categories related to their demonstrated focus and related approach: (1) improving standards for presenting and sharing clinical genetic data, (2) accelerating the development and implementation of new standards, and (3) facilitating clinical data reuse. Each project illustrates the pilot approach from inception to next steps, capturing the role of collaboration among standards development organizations, stakeholders, and end-users to ensure standards are practical and fit for purpose.

CONCLUSION

The ONC approach identifies implementation difficulties prior to broader adoption and use of standards, and provides insight into the steps needed to scale use of standards. The ONC's organization of pilot projects serves as a natural accelerator for building communities of practice, often providing a well-connected beneficiary of lessons learned.

Evaluation of Clinical Decision Support to Reduce Sedative-Hypnotic Prescribing in Older Adults

OBJECTIVE

We sought to characterize the performance of inpatient and outpatient computerized clinical decision support (CDS) alerts aimed at reducing inappropriate benzodiazepine and nonbenzodiazepine sedative medication prescribing in older adults 18 months after implementation.

METHODS

We reviewed the performance of two CDS alerts in the outpatient and inpatient settings in 2019. To examine the alerts' effectiveness, we analyzed metrics including overall alert adherence, provider-level adherence, and reasons for alert trigger and override.

RESULTS

In 2019, we identified a total of 14,534 and 4,834 alerts triggered in the outpatient and inpatient settings, respectively. Providers followed only 1% of outpatient and 3% of inpatient alerts. Most alerts were ignored (68% outpatient and 60% inpatient), while providers selected to override the remaining alerts. In each setting, the top 2% of clinicians were responsible for approximately 25% of all ignored or overridden alerts. However, a small proportion of clinicians (2% outpatient and 4% inpatient) followed the alert at least half of the time and accounted for a disproportionally large fraction of the total followed alerts. Our analysis of the free-text comments revealed that many alerts were to continue outpatient prescriptions or for situational anxiety.

CONCLUSION

Our findings highlight the importance of evaluation of CDS performance after implementation. We found large variation in response to the inpatient and outpatient alerts, both with respect to follow and ignore rates. Reevaluating the alert design by providing decision support by indication may be more helpful and may reduce alert fatigue.

Adapting Clinical Systems to Enable Adolescents' Genomic Choices

BACKGROUND

We offered adolescents personalized choices about the type of genetic results they wanted to learn during a research study and created a workflow to filter and transfer the results to the electronic health record (EHR).

METHODS

We describe adaptations needed to ensure that adolescents' results documented in the EHR and returned to adolescent/parent dyads matched their choices. A web application enabled manual modification of the underlying laboratory report data based on adolescents' choices. The final PDF format of the laboratory reports was not viewable through the EHR patient portal, so an EHR form was created to support the manual entry of discrete results that could be viewed in the portal.

RESULTS

Enabling adolescents' choices about genetic results was a labor-intensive process. More than 350 hours was required for development of the application and EHR form, as well as over 50 hours of a study professional's time to enter choices into the application and EHR. Adolescents and their parents who learned genetic results through the patient portal indicated that they were satisfied with the method of return and would make their choices again if given the option.

CONCLUSION

Although future EHR upgrades are expected to enable patient portal access to PDFs, additional improvements are needed to allow the results to be partitioned and filtered based on patient preferences. Furthermore, separating these results into more discrete components will allow them to be stored separately in the EHR, supporting the use of these data in clinical decision support or artificial intelligence applications.

Deploying Clinical Decision Support for Familial Hypercholesterolemia

Objective

Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center.

Methods

CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify "possible FH"; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS.

Results

The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine.

Conclusion

We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

A Two-Year Longitudinal Assessment of Ophthalmologists' Perceptions after Implementing an Electronic Health Record System

OBJECTIVES

To understand the attitudes and perceptions of ophthalmologists toward an electronic health record (EHR) system, before and after its clinical implementation.

METHODS

Ophthalmologists at a single large academic ophthalmology department were surveyed longitudinally before and after implementation of a new EHR system. The survey measured ophthalmologists' attitudes toward implementation of a new EHR. Questions focused on satisfaction, efficiency, and documentation. All attending physicians (between 56 and 61 at various time points) in the University of Michigan Department of Ophthalmology and Visual Sciences were surveyed. We plotted positive responses to survey questions and assessed whether perceptions followed a J-curve with an initial decrease followed by an increase surpassing pre-implementation levels.

RESULTS

Survey responses were received from 32 (52%) ophthalmologists pre-implementation, and 28 (46%) at 3 months, 35 (57%) at 7 months, 40 (71%) at 13 months and 39 (67%) at 24 months post-implementation. After EHR implementation respondents were more likely to express concerns about their ability to create high-quality documentation (p<0.01) and the impact of an electronic health record on meaningful patient interaction (p<0.01). Physicians did not report a significant change in the amount of time spent documenting outside of regular clinical work hours (p=0.54) or on their clinic efficiency and workflow (p=0.97). There was no significant change in overall job satisfaction during the study period (p=0.69). We did not observe a J-curve for any of the survey responses analyzed.

CONCLUSIONS

As ophthalmology practices continue to transition to EHRs, adapting them to their specific culture and needs is important to maintain efficiency and user satisfaction. This study identifies areas of concern to ophthalmologists that may be addressed through education of physicians and customization of software as other practices move forward with EHR implementation.

Predictors of success for electronic health record implementation in small physician practices

BACKGROUND

The federal government is promoting adoption of electronic health records (EHRs) through financial incentives for EHR use and implementation support provided by regional extension centers. Small practices have been slow to adopt EHRs.

OBJECTIVES

Our objective was to measure time to EHR implementation and identify factors associated with successful implementation in small practices receiving financial incentives and implementation support. This study is unique in exploiting quantitative implementation time data collected prospectively as part of routine project management.

METHODS

This mixed-methods study includes interviews of key informants and a cohort study of 544 practices that had worked with the Primary Care Information Project (PCIP), a publicly funded organization that since 2007 has subsidized EHRs and provided implementation support similar to that supplied by the new regional extension centers. Data from a project management database were used for a cohort study to assess time to implementation and predictors of implementation success.

RESULTS

Four hundred and thirty practices (79%) implemented EHRs within the analysis period, with a median project time of 24.7 weeks (95% CI: 23.3 - 26.4). Factors associated with implementation success were: fewer providers, practice sites, and patients; fewer Medicaid and uninsured patients; having previous experience with scheduling software; enrolling in 2010 rather than earlier; and selecting an integrated EHR plus practice management product rather than two products. Interviews identified positive attitude toward EHRs, resources, and centralized leadership as additional practice-level predictors of success.

CONCLUSIONS

A local initiative similar to current federal programs successfully implemented EHRs in primary care practices by offsetting software costs and providing implementation assistance. Nevertheless, implementation success was affected by practice size and other characteristics, suggesting that the federal programs can reduce barriers to EHR implementation but may not eliminate them.