Preparing for the spread of patient-reported outcome (PRO) data collection from primary care to community pharmacy: a mixed-methods study

Enhancing Clinicians' Use of Electronic Patient-Reported Outcome Measures in Outpatient Care: Mixed Methods Study

BACKGROUND

Despite the increasing use of patient-reported outcome measures (PROMs) for collecting self-reported data among hospital outpatients, clinicians' use of these data remains suboptimal. Insight into this issue and strategies to enhance the use of PROMs are critical but limited.

OBJECTIVE

This study aimed to examine clinicians' use of PROM data for value-based outpatient consultations and identify efforts to enhance their use of PROMs in a Dutch university hospital. First, we aimed to investigate clinicians' use of outpatients' PROM data in 2023, focusing on adoption, implementation, and maintenance. Second, we aimed to develop insights into the organizational-level strategies implemented to enhance clinicians' use of PROM data from 2020 to 2023. This included understanding the underlying rationales for these strategies and identifying strategies that appeared to be missing to address barriers or leverage facilitators. Third, we aimed to explore the key factors driving and constraining clinicians' use of PROMs in 2023.

METHODS

We integrated data from 4 sources: 1-year performance data on clinicians' use of PROMs (n=70 subdepartments), internal hospital documents from a central support team (n=56), a survey among clinicians (n=47), and interviews with individuals contributing to the organizational-level implementation of PROMs (n=20). The Reach, Effectiveness, Adoption, Implementation, and Maintenance framework was used to analyze clinicians' adoption, implementation, and maintenance of PROMs. Strategies were analyzed using the Expert Recommendations for Implementing Change taxonomy, and results were structured around the constructs of capability, opportunity, and motivation.

RESULTS

On average, around 2023, clinicians accessed PROM data for approximately 3 of 20 (14%) patients during their outpatient consultation, despite numerous strategies to improve this practice. We identified issues in adoption, implementation, and maintenance. The hospital's strategies, shaped organically and pragmatically, were related to 27 (37%) out of 73 Expert Recommendations for Implementing Change strategies. These strategies focused on enhancing clinicians' capability, opportunity, and motivation. We found shortcomings in the quality of execution and completeness of strategies in relation to addressing all barriers and leveraging facilitators. We identified variations in the factors influencing the use of PROMs among frequent PROM users, occasional users, and nonusers. Challenges to effective facilitation were apparent, with certain desired strategies being unfeasible or impeded.

CONCLUSIONS

Enhancing clinicians' use of PROMs has remained challenging despite various strategies aimed at improving their capability, opportunity, and motivation. The use of PROMs may require more substantial changes than initially expected, necessitating a shift in clinicians' professional attitudes and practices. Hospitals can facilitate rather than manage clinicians' genuine use of PROMs. They must prioritize efforts to engage clinicians with PROMs for value-based outpatient care. Specific attention to their professionalization may be warranted. Tailored strategies, designed to address within-group differences in clinicians' needs and motivation, hold promise for future efforts.

Exploring the impact of a digital health tool on patients' interaction with community pharmacists: A pilot randomized controlled study

BACKGROUND

The use of digital Patient- Reported Outcomes (PRO) tools has been shown to enhance the exchange of information and shared decision-making in medical encounters. However, their influence on patient-pharmacist interactions has not yet been explored.

OBJECTIVES

This study aimed to examine the impact of RxTalk™, a digital PRO tool, in supporting the communication between patients and pharmacists compared to usual care.

METHODS

Intervention: RxTalk™ was developed to collect information about medication adherence and beliefs, using a tablet computer.

STUDY DESIGN

A pilot randomized controlled study was conducted at a community pharmacy in Wisconsin, USA. Sixty patients were randomized to either the intervention group who used RxTalk™ during medication pick-up or the control group who did not use the tool. Patients who used RxTalk™ received paper copies of their responses which were also shared with pharmacists. The consultation was audio-recorded for both groups and coded using the Active Patient Participation Coding scale. Follow-up phone interviews were conducted with both groups within one week of enrollment.

RESULTS

Patient tapes were analyzed. In the unadjusted model, patients in the intervention group had a higher active participation rate (p = 0.004) and raised significantly more concerns during consultations (p < 0.001) compared to the control group. Pharmacists asked twice as many questions while counseling patients in the intervention group compared to the control group (p < 0.001). After controlling for patients' demographics and pharmacists' questions, there was a statistical difference between the two patient groups in their odds of expressing at least one concern utterance.

CONCLUSION

This pilot study suggests that collecting PRO from patients with chronic illnesses and providing results to pharmacists and patients can help patients express their health and medication concerns. RxTalk™ would be useful for pharmacists who wish to improve the recognition and management of medication-related problems.

Preparing community pharmacy teams for health information exchange (HIE)

BACKGROUND

Health information exchanges (HIEs) facilitate health care professionals' electronic sharing of patient information across different organizations. When community pharmacists have access to HIE, they can further contribute to improved patient outcomes. However, several implementation challenges are noted, which impede sustained pharmacist access to HIE. To our knowledge, no bidirectional HIE interface design and pharmacy team-informed implementation process has been documented. In response, our research team designed and developed an HIE interface prototype for use specifically by community pharmacy teams to access local HIE data through their pharmacy dispensing software.

OBJECTIVES

To 1) identify barriers, facilitators, and recommendations for using HIE data in community pharmacies and 2) create a curated list of resources addressing identified implementation needs to aid future implementation of a fully functional, bidirectional HIE interface by community pharmacy teams.

METHODS

Pharmacists, pharmacy technicians, and patients from three pharmacy sites within the Community Pharmacy Enhanced Services Network of Indiana participated in individual semi-structured interviews. Interview questions were mapped to select constructs across all domains of the Consolidated Framework for Implementation Research. Interview transcripts were deductively coded. A subset of participants participated in Evidence-Based Quality Improvement sessions to iteratively update planned resource items needed to support future HIE implementation.

RESULTS

We interviewed 23 total participants: 8 pharmacists, 8 pharmacy technicians, and 7 patients. Five facilitators, four barriers, and two recommendations were identified. These were further characterized into four key implementation needs: instruction on how to use HIE; guidance on workflow and team roles; resources that are patient-facing; and resources that are provider-facing, resulting in 16 planned implementation resources.

CONCLUSION

Our study provides the first-of-its-kind list of pharmacy team-informed resources to facilitate sustainability and scalability of HIE implementation in community pharmacies.

Mapping Theories, Models, and Frameworks to Evaluate Digital Health Interventions: Scoping Review

BACKGROUND

Digital health interventions (DHIs) are a central focus of health care transformation efforts, yet their uptake in practice continues to fall short of their potential. In order to achieve their desired outcomes and impact, DHIs need to reach their target population and need to be used. Many factors can rapidly intersect between this dynamic of users and interventions. The application of theories, models, and frameworks (TMFs) can facilitate the systematic understanding and explanation of the complex interactions between users, practices, technology, and health system factors that underpin research questions. There remains a gap in our understanding of how TMFs have been applied to guide the evaluation of DHIs with real-world health system operations.

OBJECTIVE

This study aims to map TMFs used in studies to guide the evaluation of DHIs. The objectives are to (1) describe the TMFs and the constructs they target, (2) identify how TMFs have been prospectively used (ie, their roles) in primary studies to evaluate DHIs, and (3) to reflect on the relevance and utility of our findings for knowledge users.

METHODS

This scoping review was conducted in partnership with knowledge users using an integrated knowledge translation approach. We included papers (eg, reports; empirical quantitative, qualitative, and mixed methods studies; conference proceedings; and dissertations) if primary insights resulting from the application of TMFs were presented. Any type of DHI was eligible. Papers published from 2000 and onward were mainly identified from the following databases: MEDLINE (Ovid), CINAHL Complete (EBSCOhost), PsycINFO (Ovid), EBM Reviews (Ovid), and Embase (Ovid).

RESULTS

A total of 156 studies published between 2000 and 2022 were included. A total of 68 distinct TMFs were identified across 85 individual studies. In more than half (85/156, 55%) of the included studies, 1 of following 6 prevailing TMFs were reported: Consolidated Framework for Implementation Research (n=39); the Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework (n=17); the Technology of Acceptance Model (n=16); the Unified Theory on Acceptance and Use of Technology (n=12); the Diffusion of Innovation Theory (n=10); and Normalization Process Theory (n=9). The most common intended roles of the 6 TMFs were to inform data collection (n=86), to inform data analysis (n=69), and to identify key constructs that may serve as barriers and facilitators (n=52).

CONCLUSIONS

As TMFs are most often reported to be applied to support data collection and analysis, researchers should consider more clearly synthesizing key insights as practical use cases to both increase the relevance and digestibility of their findings. There is also a need to adapt or develop guidelines for better reporting DHIs and the use of TMFs to guide evaluation. Hence, it would contribute to ensuring ongoing technology transformation efforts are evidence and theory informed rather than anecdotally driven.

Evidence-Based Quality Improvement (EBQI) in the pre-implementation phase: key steps and activities

Background

Evidence-Based Quality Improvement (EBQI) involves researchers and local partners working collaboratively to support the uptake of an evidence-based intervention (EBI). To date, EBQI has not been consistently included in community-engaged dissemination and implementation literature. The purpose of this paper is to illustrate the steps, activities, and outputs of EBQI in the pre-implementation phase.

Methods

The research team applied comparative case study methods to describe key steps, activities, and outputs of EBQI across seven projects. Our approach included: (1) specification of research questions, (2) selection of cases, (3) construction of a case codebook, (4) coding of cases using the codebook, and (5) comparison of cases.

Results

The cases selected included five distinct settings (e.g., correction facilities, community pharmacies), seven EBIs (e.g., nutrition promotion curriculum, cognitive processing therapy) and five unique lead authors. Case examples include both community-embedded and clinically-oriented projects. Key steps in the EBQI process included: (1) forming a local team of partners and experts, (2) prioritizing implementation determinants based on existing literature/data, (3) selecting strategies and/or adaptations in the context of key determinants, (4) specifying selected strategies/adaptations, and (5) refining strategies/adaptations. Examples of activities are included to illustrate how each step was achieved. Outputs included prioritized determinants, EBI adaptations, and implementation strategies.

Conclusions

A primary contribution of our comparative case study is the delineation of various steps and activities of EBQI, which may contribute to the replicability of the EBQI process across other implementation research projects.