Literature DB >> 30032965

Application of contextual design methods to inform targeted clinical decision support interventions in sub-specialty care environments.

Anne Miller1, Jejo D Koola2, Michael E Matheny3, Julie H Ducom4, Jason M Slagle1, Erik J Groessl5, Freneka F Minter3, Jennifer H Garvin6, Matthew B Weinger1, Samuel B Ho7.   

Abstract

BACKGROUND &
OBJECTIVES: In healthcare, the routine use of evidence-based specialty care management plans is mixed. Targeted computerized clinical decision support (CCDS) interventions can improve physician adherence, but adoption depends on CCDS' 'fit' within clinical work. We analyzed clinical work in outpatient and inpatient settings as a basis for developing guidelines for optimizing CCDS design.
METHODS: The contextual design approach guided data collection, collation and analysis. Forty (40) consenting physicians were observed and interviewed in general internal medicine inpatient units and gastroenterology (GI) outpatient clinics at two academic medical centers. Data were collated using interpretive debriefing, and consolidated using thematic analysis and three work modeling approaches (communication flow, sequence and artifact models).
RESULTS: Twenty-six consenting physicians were observed at Site A and 14 at Site B. Observations included attending (33%) and resident physicians. During research team debriefings, 220 of 341 unique topics were categorized into 5 CCDS-relevant themes. Resident physicians relied on patient assessment & planning processes to support their roles as communication and coordination hubs within the medical team. Artifact analysis further elucidated the evolution of assessment and planning over work shifts.
CONCLUSIONS: The usefulness of CCDS tools may be enhanced in clinical care if the design: 1) accounts for clinical work that is distributed across people, space, and time; 2) targets communication and coordination hubs (specific roles) that can amplify the usefulness of CCDS interventions; 3) integrates CCDS with early clinical assessment & planning processes; and 4) provides CCDS in both electronic & hardcopy formats. These requirements provide a research agenda for future research in clinician-CCDS integration. Published by Elsevier B.V.

Entities:  

Keywords:  Cirrhosis; Computerized clinical decision support; Contextual design methods; Human factors engineering; Specialty care

Mesh:

Year:  2018        PMID: 30032965     DOI: 10.1016/j.ijmedinf.2018.05.005

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  5 in total

1.  Considerations for Designing EHR-Embedded Clinical Decision Support Systems for Antimicrobial Stewardship in Pediatric Emergency Departments.

Authors:  Mustafa Ozkaynak; Noel Metcalf; Daniel M Cohen; Larissa S May; Peter S Dayan; Rakesh D Mistry
Journal:  Appl Clin Inform       Date:  2020-09-09       Impact factor: 2.342

2.  CYP2D6 Genotype-guided Metoprolol Therapy in Cardiac Surgery Patients: Rationale and Design of the Pharmacogenetic-guided Metoprolol Management for Postoperative Atrial Fibrillation in Cardiac Surgery (PREEMPTIVE) Pilot Study.

Authors:  Wills C Dunham; Matthew B Weinger; Jason Slagle; Mias Pretorius; Ashish S Shah; Tarek S Absi; Matthew S Shotwell; Marc Beller; Erica Thomas; Cindy L Vnencak-Jones; Robert E Freundlich; Jonathan P Wanderer; Warren S Sandberg; Miklos D Kertai
Journal:  J Cardiothorac Vasc Anesth       Date:  2019-09-10       Impact factor: 2.628

3.  Human Factors and Sociotechnical Issues.

Authors:  Sylvia Pelayo; Yalini Senathirajah
Journal:  Yearb Med Inform       Date:  2019-08-16

4.  Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

Authors:  Jennifer Hornung Garvin; Julie Ducom; Michael Matheny; Anne Miller; Dax Westerman; Carrie Reale; Jason Slagle; Natalie Kelly; Russ Beebe; Jejo Koola; Erik J Groessl; Emily S Patterson; Matthew Weinger; Amy M Perkins; Samuel B Ho
Journal:  JMIR Med Inform       Date:  2019-07-03

5.  Clinician checklist for assessing suitability of machine learning applications in healthcare.

Authors:  Ian Scott; Stacey Carter; Enrico Coiera
Journal:  BMJ Health Care Inform       Date:  2021-02
  5 in total

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