Clinicians' Values and Preferences for Medication Adherence and Cost Clinical Decision Support in Primary Care: A Qualitative Study

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care

OBJECTIVES

 The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design.

METHODS

 We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts.

RESULTS

 We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality.

CONCLUSION

 Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.

Health informatics interventions to minimize out-of-pocket medication costs for patients: what providers want

Objective

To explore diverse provider perspectives on: strategies for addressing patient medication cost barriers; patient medication cost information gaps; current medication cost-related informatics tools; and design features for future tool development.

Materials and Methods

We conducted 38 semistructured interviews with providers (physicians, nurses, pharmacists, social workers, and administrators) in a Midwestern health system in the United States. We used 3 rounds of qualitative coding to identify themes.

Results

Providers lacked access to information about: patients’ ability to pay for medications; true costs of full medication regimens; and cost impacts of patient insurance changes. Some providers said that while existing cost-related tools were helpful, they contained unclear insurance information and several questioned the information’s quality. Cost-related information was not available to everyone who needed it and was not always available when needed. Fragmentation of information across sources made cost-alleviation information difficult to access. Providers desired future tools to compare medication costs more directly; provide quick references on costs to facilitate clinical conversations; streamline medication resource referrals; and provide centrally accessible visual summaries of patient affordability challenges.

Discussion

These findings can inform the next generation of informatics tools for minimizing patients’ out-of-pocket costs. Future tools should support the work of a wider range of providers and situations and use cases than current tools do. Such tools would have the potential to improve prescribing decisions and better link patients to resources.

Conclusion

Results identified opportunities to fill multidisciplinary providers’ information gaps and ways in which new tools could better support medication affordability for patients.

Almost a quarter of Americans taking prescription medications have difficulty affording them. We asked 38 healthcare providers what they do to help patients get affordable medications. They try to reduce the number of medications that patients take, choose more affordable medication options, and connect them to free medications or financial help. But it is hard for providers to do these things because they don’t always know which patients have financial challenges, and they may not know how much medications cost patients. Healthcare providers use digital tools like ordering systems to pick medications for patients, but they do not always have clear price information and they do not help outside of healthcare visits with prescribers. It is also hard for healthcare providers to get information about what patients have difficulty affording medications, and about resources to help them. Healthcare providers want new and improved digital tools to help them choose medications, and to be able to compare exact medication price differences. They also want a visual sign for patients with financial challenges, and centralized information about cost reduction resources. Finally, they desire tools to help them talk to patients about mediation prices, and medication price reports for patients themselves.

Low Efficacy of Medication Shortage Clinical Decision Support Alerts

OBJECTIVE

 We examined clinical decision support (CDS) alerts designed specifically for medication shortages to characterize and assess provider behavior in response to these short-term clinical situations.

MATERIALS AND METHODS

 We conducted a retrospective analysis of the usage of medication shortage alerts (MSAs) that included at least one alternative medication suggestion and were active for 60 or more days during the 2-year study period, January 1, 2018 to December 31, 2019, in a large health care system. We characterized ordering provider behavior in response to inpatient MSAs. We then developed a linear regression model to predict provider response to alerts using the characteristics of the ordering provider and alert frequency groupings.

RESULTS

 During the study period, there were 67 MSAs in use that focused on 42 distinct medications in shortage. The MSAs suggested an average of 3.9 alternative medications. Adjusting for the different alerts, fellows (p = 0.004), residents (p = 0.03), and physician assistants (p = 0.02) were less likely to accept alerts on average compared with attending physicians. Further, female ordering clinicians (p < 0.001) were more likely to accept alerts on average compared with male ordering clinicians.

CONCLUSION

 Our findings demonstrate that providers tended to reject MSAs, even those who were sometimes flexible about their responses. The low overall acceptance rate supports the theory that alerts appearing at the time of order entry may have limited value, as they may be presented too late in the decision-making process. Though MSAs are designed to be attention-grabbing and higher impact than traditional CDS, our findings suggest that providers rarely change their clinical decisions when presented with these alerts.

Human Factors and Organizational Issues in Health Informatics: Innovations and Opportunities

OBJECTIVE

Human factors and ergonomics (HF/E) frameworks and methods are becoming embedded in the health informatics community. There is now broad recognition that health informatics tools must account for the diverse needs, characteristics, and abilities of end users, as well as their context of use. The objective of this review is to synthesize the current nature and scope of HF/E integration into the health informatics community.

METHODS

Because the focus of this synthesis is on understanding the current integration of the HF/E and health informatics research communities, we manually reviewed all manuscripts published in primary HF/E and health informatics journals during 2020.

RESULTS

HF/E-focused health informatics studies included in this synthesis focused heavily on EHR customizations, specifically clinical decision support customizations and customized data displays, and on mobile health innovations. While HF/E methods aimed to jointly improve end user safety, performance, and satisfaction, most HF/E-focused health informatics studies measured only end user satisfaction.

CONCLUSION

HF/E-focused health informatics researchers need to identify and communicate methodological standards specific to health informatics, to better synthesize findings across resource intensive HF/E-focused health informatics studies. Important gaps in the HF/E design and evaluation process should be addressed in future work, including support for technology development platforms and training programs so that health informatics designers are as diverse as end users.

Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome

OBJECTIVE

Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.

METHODS

We examined provider response data over a period from October 8, 2016 until November 8, 2018 for a CDS alert deployed within the EHR from a 13-hospital integrated health care system that fires when a patient with a QTc ≥ 500 ms within the past 14 days is prescribed a known QT-prolonging medication. We used multivariate generalized estimating equations to analyze the impact of therapeutic alternatives, relative risk of diLQTS for specific medications, and patient characteristics on provider response to the CDS and overall patient mortality.

RESULTS

The CDS alert fired 15,002 times for 7,510 patients for which the most common response (51.0%) was to override the alert and order the culprit medication. In multivariate models, we found that patient age, relative risk of diLQTS, and presence of alternative agents were significant predictors of adherence to the CDS alerts and that nonadherence itself was a predictor of mortality. Risk of diLQTS and presence of an alternative agent are major factors in provider adherence to a CDS to prevent diLQTS; however, provider nonadherence was associated with a decreased risk of mortality.

CONCLUSION

Surrogate endpoints, such as provider adherence, can be useful measures of CDS value but attention to hard outcomes, such as mortality, is likely needed.

Reducing Inappropriate Outpatient Medication Prescribing in Older Adults across Electronic Health Record Systems

BACKGROUND

The American Geriatrics Society recommends against the use of certain potentially inappropriate medications (PIMs) in older adults. Prescribing of these medications correlates with higher rates of hospital readmissions, morbidity, and mortality. Vanderbilt University Medical Center previously deployed clinical decision support (CDS) to decrease PIM prescribing rates, but recently transitioned to a new electronic health record (EHR).

OBJECTIVE

The goal of this study was to evaluate PIM prescribing rates for older adults before and after migration to the new EHR system.

METHODS

We reviewed prescribing rates of PIMs in adults 65 years and older, normalized per 100 total prescriptions from the legacy and new EHR systems between July 1, 2014 and December 31, 2019. The PIM prescribing rates before and after EHR migration during November 2017 were compared using a U-chart and Poisson regression model. Secondary analysis descriptively evaluated the frequency of prescriber acceptance rates in the new EHR.

RESULTS

Prescribing rates of PIMs decreased 5.2% (13.5 per 100 prescriptions to 12.8 per 100 prescriptions; p < 0.0001) corresponding to the implementation of alternatives CDS in the legacy EHR. After migration of the alternative CDS from the legacy to the new EHR system, PIM prescribing rates dropped an additional 18.8% (10.4 per 100 prescriptions; p < 0.0001). Acceptance rates of the alternative recommendations for PIMs was low overall at 11.1%.

CONCLUSION

The prescribing rate of PIMs in adults aged 65 years and older was successfully decreased with the implementation of prescribing CDS. This decrease was not only maintained but strengthened by the transition to a new EHR system.