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Record W4387077933 · doi:10.1002/jhm.13203

A crowdsourced campaign in IT optimization to improve ease of practice

2023· article· en· W4387077933 on OpenAlexaboutno aff
Sonya Makhni, Bree Andrews, Simone Maxey, Kayla Scales, Sean Bernstein, Peter Georginis, Sachin Shah, Cheng Kai Kao, Stephen G. Weber, Vineet M. Arora

Bibliographic record

VenueJournal of Hospital Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityMedicineBurnoutHealth careWorkforceOddsHealth information technologyPsychological interventionDisengagement theoryIntervention (counseling)NursingMedical educationGerontology

Abstract

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Improving the culture of wellness, ease of practice, and sense of professional fulfillment are cornerstone components to achieving physician well-being. Clinicians are expected to take extraordinary care of patients despite being faced with increased inefficiencies and administrative burdens across healthcare organizations.1-4 Information technology contributes to inefficiencies within clinical practice, which ultimately results in a downstream negative impact on clinicians caused by practice disruption, dissatisfaction, and overall provider disengagement and burnout. Roughly 60% of physicians state that the electronic health record (EHR) reduces professional satisfaction and/or needs a complete overhaul.5 Further, several studies have demonstrated the significant time burden that EHRs place on physicians. EHR usability and functionality are consistently poor, scoring 9%, which is considerably below that of many everyday technologies.6 In fact, there is a dose-dependent relationship between EHR usability scores and the odds of burnout—every one point higher (more favorable) on the System Usability Score correlates with 3% lower odds of burnout.6 Systemic improvements require a more nuanced and comprehensive understanding of the deficiencies surrounding healthcare-associated technology from the perspectives of frontline clinicians so that we can begin to make concerted efforts to improve inefficiencies that lead to dissatisfaction and reduced well-being. Without feedback from our healthcare workforce, we may overlook mechanisms that can ultimately improve healthcare-related technologies that serve clinicians and patients alike. The team from Hawaii Pacific Health launched the first known campaign of this kind, known as “Getting Rid of Stupid Stuff.”7 This intervention targeted inefficient and cumbersome documentation processes in an effort to decrease unnecessary tasks and save clinicians time. Employees were asked to identify facets of the documentation process that they felt were poorly designed or superfluous and then nominate these inefficiencies to the program. Nominations were related to documentation requirements that were unnecessary, inefficient, or confusing. Such nominations led to key insights that enabled leadership to either address or eliminate various documentation requirements or provide education to clinicians on how to more effectively leverage tools within the EHR that could improve their workflow efficiencies. Although limited in scope to documentation-specific tasks, this program demonstrated a very important initial step in engaging with frontline clinicians to improve EHR-related inefficiencies.7 The IDEA study outlined by Strudwick et al. is an ongoing effort to specifically engage nurses in generating ideas to improve EHR-related inefficiencies specific to their workflows at two Canadian facilities.8 Ultimately, the study aims to better understand EHR usage patterns and documentation requirements to determine the greatest sources of burden. Their team uses EHR analytics and focus group processes to improve documentation, chart review, and medication reconciliation. While similar programs to combat IT-related burnout are limited, there is a clear emerging need. The #WhatToFix program, a newly developed campaign at the University of Chicago, is a novel program for physicians and advanced practice providers (APPs) designed to: (1) engage clinicians to improve ease of practice and reduce daily IT-related frustrations; (2) introduce a practical “fix” system that optimizes the practice of medicine; (3) analyze commonly reported and recurring themes to escalate priority fixes; and (4) highlight notable successes of the program via a broadcast system. Submissions to the #WhatToFix program were solicited through a six-question intake form via Redcap, which was disseminated to physicians and APPs at periodic intervals. This included two free-text fields for the user to describe the relevant problem as well as a proposed solution. Submissions were routed to a core team: a program manager, a clinical informatics (CI) fellow, health informaticists, and a supervisor with high visibility at the organization (Chief Medical Officer, Chief Wellness Officer, Clinical Learning Environment Review, Designated Institutional Officer) to evaluate submissions with the goal of selecting one submission per month to operationalize. Submissions were evaluated for feasibility in becoming operationalized within 1–2 months, and projected impact alongside alignment with institutional goals. The team committed to one of the following actions based on various phenotypes: (1) exploring a fix that uses multiple services and processes to improve practice, (2) sending the fix to an analyst or builder for quick resolution, (3) moving the fix to a leadership discussion for broader consideration, (4) acknowledging the value of the fix but realizing it is not feasible now. The #WhatToFix program received a total of 378 submissions from July 1, 2019 to March 17, 2021. Most submissions (63%) were related to the EHR or IT issues. Submissions surrounding operations at the hospital and patient level came next at 20% and 16%, respectively This breakdown also demonstrates the numerous submissions received from outside the EHR/IT categories. From phlebotomy processes to parking, call rooms, and paging—clinicians engaged with #WhatToFix on issues unrelated to the EHR (Table 1). The program welcomed this engagement, as no centralized mechanism existed to report and provide feedback for operational problems pertaining to the hospital. To address non-IT related submissions, the #WhatToFix team worked to liaison with various leaders across different departments within the health system. Among the submissions that were resolved and operationalized, a select number were chosen to be broadcasted to the entire clinical community of MDs and APPs, monthly. Broadcasts highlighted not only the problem and operationalized solution but also recognized the individuals involved in identifying and developing the positive changes. Broadcasts also included screenshots, job aids, and explanations of the fixes. The #WhatToFix received three requests for fixes from residents, or our frontline teams, around the problem of single author discharge summaries that siloed communication to one provider despite handoffs, and multidisciplinary and subspecialty involvement in patient care. One fixer wrote “discharge summaries are often used of a depository of information and become unwieldy and un-usable as teams provide hand offs of patients before discharge.” The CI and CMIO leaders agreed that the ‘shared discharge summary’ would help minimize note bloat, uses timely information during service handoffs and highlights that discharge planning starts early and continues throughout the hospitalization. Using our analytics teams, a new system was created based on the proposed solution of our users. Now, there is a “share note” pathway for discharge summaries across the organization that can be implemented as soon as the first author starts writing. This fix was broadcast in a monthly email, generating support for the process and putting key notes about the fix increasing the use of the fix at the organizational level (Figure 1). The crowdsourced #WhatToFix platform has been a successful pilot program in engaging physicians and APPs to suggest IT and non-IT solutions to system-level problems. Many of these operationalized solutions have improved ease of practice, revealed knowledge or practice gaps, and ultimately promoted engagement between clinicians, CI, and IT. Operationalizing the #WhatToFix program revealed several important considerations. A significant challenge is the issue of limited resources facing every healthcare system. The small team size, restrictions on team-member time, and lack of financial resources limited the program's ability to expand beyond the present scope. Expanding the program to employees across the institution, program automation, and implementation of submission tracking would require a designated allotment of time and resources to the program. In addition, a budget should be allocated specifically for such programs, allowing them to function independently from normal IT operations, similar to how a help desk functions for basic matters like connectivity or password resets. The #WhattoFix program is an efficient and effective way for clinicians to report problems or propose solutions; #WhatToFix has served as a direct conduit between the clinical workforce to health system leadership. The crowdsourcing nature of the program has led to enthusiasm and a climate of collaboration for solutions. With appropriate and effective intra- and interdepartmental triage, permanent solutions to problems were developed and disseminated to the entire community, improving practice beyond the EHR. The authors declare no conflict of interest.

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)0.002
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.347
Teacher spread0.336 · 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 designNot applicable
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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