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Record W3112739210 · doi:10.2196/22319

Improving Primary Care Medication Processes by Using Shared Electronic Medication Plans in Switzerland: Lessons Learned From a Participatory Action Research Study

2020· article· en· W3112739210 on OpenAlexvenueno aff
Benjamin Bugnon, Antoine Geissbühler, Thomas Bischoff, Pascal Bonnabry, Christian von Plessen

Bibliographic record

VenueJMIR Formative Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchPrimary careAction researchAction (physics)Citizen journalismPsychologyMedicineMedical educationFamily medicineComputer scienceSociologyWorld Wide WebPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Several countries have launched health information technology (HIT) systems for shared electronic medication plans. These systems enable patients and health care professionals to use and manage a common list of current medications across sectors and settings. Shared electronic medication plans have great potential to improve medication management and patient safety, but their integration into complex medication-related processes has proven difficult, and there is little scientific evidence to guide their implementation. OBJECTIVE: The objective of this paper is to summarize lessons learned from primary care professionals involved in a pioneering pilot project in Switzerland for the systemwide implementation of shared electronic medication plans. We collected experiences, assessed the influences of the local context, and analyzed underlying mechanisms influencing the implementation. METHODS: In this formative action research study, we followed 5 clusters of health care professionals during 6 months. The clusters represented rural and urban primary care settings. A total of 18 health care professionals (primary care physicians, pharmacists, and nurses) used the pilot version of a shared electronic medication plan on a secure web platform, the precursor of Switzerland's electronic patient record infrastructure. We undertook 3 group interviews with each of the 5 clusters, analyzed the content longitudinally and across clusters, and summarized it into lessons learned. RESULTS: Participants considered medication plan management, digitalized or not, a core element of good clinical practice. Requirements for the successful implementation of a shared electronic medication plan were the integration into and simplification of clinical routines. Participants underlined the importance of an enabling setting with designated reference professionals and regular high-quality interactions with patients. Such a setting should foster trusting relationships and nurture a culture of safety and data privacy. For participants, the HIT was a necessary but insufficient building block toward better interprofessional communication, especially in transitions. Despite oral and written information, the availability of shared electronic medication plans did not generate spontaneous demand from patients or foster more engagement in their medication management. The variable settings illustrated the diversity of medication management and the need for local adaptations. CONCLUSIONS: The results of our study present a unique and comprehensive description of the sociotechnical challenges of implementing shared electronic medication plans in primary care. The shared ownership among multiple stakeholders is a core challenge for implementers. No single stakeholder can build and maintain a safe, usable HIT system with up-to-date medication information. Buy-in from all involved health care professionals is necessary for consistent medication reconciliation along the entire care pathway. Implementers must balance the need to change clinical processes to achieve improvements with the need to integrate the shared electronic medication plan into existing routines to facilitate adoption. The lack of patient involvement warrants further study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.465
GPT teacher head0.598
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2020
Admission routes1
Has abstractyes

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