Evidence map›Paper›PMID 28646886›Full record

ArticleImplementation science : IS2017

Implementation science for ambulatory care safety: a novel method to develop context-sensitive interventions to reduce quality gaps in monitoring high-risk patients.

Kathryn M McDonald, George Su, Sarah Lisker, Emily S Patterson, Urmimala Sarkar

Open access · goldAbstract read
In one paragraph

Article in Implementation science : IS, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Kathryn M McDonaldUniversity of California Berkeley, School of Public Health, 50 University Hall, Berkeley, 94720, CA, USA. Kathy.McDonald@stanford.edu.
George SuDepartment of Medicine, School of Medicine, University of California San Francisco, 1001 Potrero Avenue, San Francisco, 94110, CA, USA.
Sarah LiskerDepartment of Medicine, School of Medicine, University of California San Francisco, 1001 Potrero Avenue, San Francisco, 94110, CA, USA.
Emily S PattersonOhio State University, College of Medicine, School of Health and Rehabilitation Sciences, Division of Health Information Management and Systems, 453 W 10th Ave, Columbus, 43210, OH, USA.
Urmimala SarkarDepartment of Medicine, School of Medicine, University of California San Francisco, 1001 Potrero Avenue, San Francisco, 94110, CA, USA.
University of California, San Francisco · USOhio Department of Health · USStanford University · US

Funding

Building an Ambulatory Patient Safety Learning Laboratory for Diverse PopulationsP30HS023558 · AHRQ · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SARKAR, URMIMALA · 2014 to 2017
$3.9M
AHRQ HHS P30 HS023558
6 · The paper itself

Abstract

backgroundMissed evidence-based monitoring in high-risk conditions (e.g., cancer) leads to delayed diagnosis. Current technological solutions fail to close this safety gap. In response, we aim to demonstrate a novel method to identify common vulnerabilities across clinics and generate attributes for context-flexible population-level monitoring solutions for widespread implementation to improve quality.

methodsBased on interviews with staff in otolaryngology, pulmonary, urology, breast, and gastroenterology clinics at a large urban publicly funded health system, we applied journey mapping to co-develop a visual representation of how patients are monitored for high-risk conditions. Using a National Academies framework and context-sensitivity theory, we identified common systems vulnerabilities and developed preliminary concepts for improving the robustness for monitoring patients with high-risk conditions ("design seeds" for potential solutions). Finally, we conducted a face validity and prioritization assessment of the design seeds with the original interviewees.

resultsWe identified five high-risk situations for potentially consequential diagnostic delays arising from suboptimal patient monitoring. All situations related to detection of cancer (head and neck, lung, prostate, breast, and colorectal). With clinic participants we created 5 journey maps, each representing specialty clinic workflow directed at evidence-based monitoring. System vulnerabilities common to the different clinics included challenges with: data systems, communications handoffs, population-level tracking, and patient activities. Clinic staff ranked 13 design seeds (e.g., keep patient list up to date, use triggered notifications) addressing these vulnerabilities. Each design seed has unique evaluation criteria for the usefulness of potential solutions developed from the seed.

conclusionsWe identified and ranked 13 design seeds that characterize situations that clinicians described 'wake them up at night', and thus could reduce their anxiety, save time, and improve monitoring of high-risk patients. We anticipate that the design seed approach promotes robust and context-sensitive solutions to safety and quality problems because it provides a human-centered link between the experienced problem and various solutions that can be tested for viability. The study also demonstrates a novel integration of industrial and human factors methods (journey mapping, process tracing and design seeds) linked to implementation theory for use in designing interventions that anticipate and reduce implementation challenges.

Indexed as

Quality of Health CareAmbulatory CareDelayed DiagnosisEvidence-Based MedicineHealth Plan ImplementationHumansNeoplasmsPatient SafetyReproducibility of ResultsSan FranciscoAmbulatory careCancerDesign seedsDiagnostic errorHuman factorsJourney mappingOrganizational interventionsPatient monitoringPatient safety

Identifiers

PMID28646886
PMCPMC5483297
OpenAlexW2636353004

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.