Dataset Information

Using Electronic Clinical Decision Support to Examine Vision Rehabilitation Referrals and Practice Guidelines in Ophthalmology.

ABSTRACT:

Purpose

To examine ophthalmologist use of an electronic health record (EHR)-based clinical decision support system (CDSS) to facilitate low vision rehabilitation (LVR) care referral.

Methods

The CDSS alert was designed to appear when best documented visual acuity was <20/40 or hemianopia or quadrantanopia diagnosis was identified during an ophthalmology encounter from November 6, 2017, to April 5, 2019. Fifteen ophthalmologists representing eight subspecialties from an academic medical center were required to respond to the referral recommendation (order, don't order). LVR referral rates and ophthalmologist user experience were assessed. Encounter characteristics associated with LVR referrals were explored using multilevel logistic regression analysis.

Results

The alert appeared for 3625 (8.9%) of 40,931 eligible encounters. The referral rate was 14.8% (535/3625). Of the 3413 encounters that met the visual acuity criterion only, patients who were worse than 20/60 were more likely to be referred, and 32.4% of referred patients were between 20/40 and 20/60. Primary reasons for deferring referrals included active medical or surgical treatment, refractive-related issues, and previous connection to LVR services. Eleven of the 13 ophthalmologists agreed that the alert was useful in identifying candidates for LVR services.

Conclusions

A CDSS for patient identification and referral offers an acceptable mechanism to apply practice guidelines and prompt ophthalmologists to facilitate LVR care. Further study is warranted to optimize ophthalmologist user experience while refining alert criteria beyond visual acuity.

Translational relevance

The CDSS provides the framework for multi-center research to assess the development of pragmatic algorithms and standards for facilitating LVR care.

SUBMITTER: Goldstein JE

PROVIDER: S-EPMC9547361 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using Electronic Clinical Decision Support to Examine Vision Rehabilitation Referrals and Practice Guidelines in Ophthalmology.

Goldstein Judith E JE Guo Xinxing X Swenor Bonnielin K BK Boland Michael V MV Smith Kerry K

Translational vision science & technology 20221001 10

<h4>Purpose</h4>To examine ophthalmologist use of an electronic health record (EHR)-based clinical decision support system (CDSS) to facilitate low vision rehabilitation (LVR) care referral.<h4>Methods</h4>The CDSS alert was designed to appear when best documented visual acuity was <20/40 or hemianopia or quadrantanopia diagnosis was identified during an ophthalmology encounter from November 6, 2017, to April 5, 2019. Fifteen ophthalmologists representing eight subspecialties from an academic me ...[more]

PMID: 36180024

Similar Datasets

Project description:ImportanceElectronic clinical decision support systems apply clinical guidelines in real time and offer a new approach to improve referral and utilization of low vision rehabilitation (LVR) care.ObjectiveTo characterize patients and factors associated with LVR service utilization with and without the use of an electronic health record (EHR) clinical decision support system (CDSS) alert.Design, setting, and participantsQuality improvement study using EHR data to compare patients who did and did not utilize LVR service after referral between November 6, 2017, and October 5, 2019, (primary) and to assess overall service utilization rate from September 1, 2016, to April 2, 2021, regardless of referral status (secondary). Participants in the primary analysis were patients at a large ophthalmology department in an academic medical center in the US who received an LVR referral recommendation from their ophthalmologist according to the CDSS alert. The secondary analysis included patients with best documented visual acuity (BDVA) worse than 20/40 before, during, and after the CDSS implementation. Data were analyzed from August 2021 to April 2022.ExposuresNumber and locations of referral recommendations for LVR service according to the CDSS alert in the primary analysis; active CDSS implementation in the secondary analysis.Main outcomes and measuresLVR service utilization rate was defined as the number of patients who accessed service among those who were referred (primary) and among those with BDVA worse than 20/40 (secondary). EHR data on patient demographics (age, sex, race, ethnicity) and ophthalmology encounter characteristics (numbers of referral recommendations, encounter location, and BDVA) were extracted.ResultsOf the 429 patients (median [IQR] age, 71 [53 to 83] years; 233 female [54%]) who received a CDSS-based referral recommendation, 184 (42.9%) utilized LVR service. Compared with nonusers of LVR, users were more likely to have received at least 2 referral recommendations (12.5% vs 6.1%; χ21 = 5.29; P = .02) and at an ophthalmology location with onsite LVR service (87.5% vs 78.0%; χ21 = 6.50; P = .01). Onsite LVR service (odds ratio, 2.06; 95% CI, 1.18-3.61) persisted as the only statistically significant factor after adjusting for patient demographics and other referral characteristics. Among patients whose BDVA was worse than 20/40 before, during, and after the CDSS implementation regardless of referral status, the LVR service utilization rate was 6.1%, 13.8%, and 7.5%, respectively.Conclusions and relevanceIn this quality improvement study, ophthalmologist referral recommendations and onsite LVR services at the location where patients receive other ophthalmic care were significantly associated with service utilization. Ophthalmology CDSSs are promising tools to apply clinical guidelines in real time to improve connection to care.

Project description:Importance: Early mobilization, out-of-bed activity, is a component of acute stroke unit care; however, stroke patient heterogeneity requires complex decision-making. Clinically credible and applicable CPGs are needed to support and optimize the delivery of care. In this study, we are specifically exploring the role of clinical practice guidelines to support individual patient-level decision-making by stroke clinicians about early mobilization post-stroke. Methods: Our study uses a novel, two-pronged approach. (1) A review of CPGs containing recommendations for early mobilization practices published after 2015 was appraised using purposely selected items from the Appraisal of Guidelines Research and Evaluation-Recommendations Excellence (AGREE-REX) tool relevant to decision-making for clinicians. (2) A cross-sectional study involving semi-structured interviews with Australian expert stroke clinicians representing content experts and CPG target users. Every CPG was independently assessed against the AGREE-REX standard by two reviewers. Expert stroke clinicians, invited via email, were recruited between June 2019 to March 2020.The main outcomes from the review was the proportion of criteria addressed for each AGREE-REX item by individual and all CPG(s). The main cross-sectional outcomes were the distributions of stroke clinicians' responses about the utility of CPGs, specific areas of uncertainty in early mobilization decision-making, and suggested parameters for inclusion in future early mobilization CPGs. Results: In 18 identified CPGs, many did not adequately address the "Evidence" and "Applicability to Patients" AGREE-REX items. Out of 30 expert stroke clinicians (11 physicians [37%], 11 physiotherapists [37%], 8 nurses [26%]; median [IQR] years of experience, 14 [10-25]), 47% found current CPGs "too broad or vague," while 40% rely on individual clinical judgement and interpretation of the evidence to select an evidence-based choice of action. The areas of uncertainty in decision-making revealed four key suggestions: (1) more granular descriptions of patient and stroke characteristics for appropriate tailoring of decisions, (2) clear statements about when clinical flexibility is appropriate, (3) detailed description of the intervention dose, and (4) physical assessment criteria including safety parameters. Conclusions: The lack of specificity, clinical applicability, and adaptability of current CPGs to effectively respond to the heterogeneous clinical stroke context has provided a clear direction for improvement.

Project description:ObjectivesThe use of electronic health record (EHR)-embedded child abuse clinical decision support (CA-CDS) may help decrease morbidity from child maltreatment. We previously reported on the development of CA-CDS in Epic and Allscripts. The objective of this study was to implement CA-CDS into Epic and Allscripts and determine its effects on identification, evaluation, and reporting of suspected child maltreatment.Materials and methodsAfter a preimplementation period, CA-CDS was implemented at University of Wisconsin (Epic) and Northwell Health (Allscripts). Providers were surveyed before the go-live and 4 months later. Outcomes included the proportion of children who triggered the CA-CDS system, had a positive Child Abuse Screen (CAS) and/or were reported to Child Protective Services (CPS).ResultsAt University of Wisconsin (UW), 3.5% of children in the implementation period triggered the system. The CAS was positive in 1.8% of children. The proportion of children reported to CPS increased from 0.6% to 0.9%. There was rapid uptake of the abuse order set.At Northwell Health (NW), 1.9% of children in the implementation period triggered the system. The CAS was positive in 1% of children. The child abuse order set was rarely used. Preimplementation, providers at both sites were similar in desire to have CA-CDS system and perception of CDS in general. After implementation, UW providers had a positive perception of the CA-CDS system, while NW providers had a negative perception.DiscussionCA-CDS was able to be implemented in 2 different EHRs with differing effects on clinical care and provider feedback. At UW, the site with higher uptake of the CA-CDS system, the proportion of children who triggered the system and the rate of positive CAS was similar to previous studies and there was an increase in the proportion of cases of suspected abuse identified as measured by reports to CPS. Our data demonstrate how local environment, end-users' opinions, and limitations in the EHR platform can impact the success of implementation.ConclusionsWhen disseminating CA-CDS into different hospital systems and different EHRs, it is critical to recognize how limitations in the functionality of the EHR can impact the success of implementation. The importance of collecting, interpreting, and responding to provider feedback is of critical importance particularly with CDS related to child maltreatment.

Project description:BackgroundOpioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients.MethodsHere we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools.ResultsThe iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools.ConclusionsUse of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.

Project description:ObjectiveTo evaluate effectiveness, safety and cost-effectiveness of Computerised Clinical Decision Support (CCDS) for paramedics attending older people who fall.DesignCluster trial randomised by paramedic; modelling.Setting13 ambulance stations in two UK emergency ambulance services.Participants42 of 409 eligible paramedics, who attended 779 older patients for a reported fall.InterventionsIntervention paramedics received CCDS on Tablet computers to guide patient care. Control paramedics provided care as usual. One service had already installed electronic data capture.Main outcome measuresEffectiveness: patients referred to falls service, patient reported quality of life and satisfaction, processes of care.SafetyFurther emergency contacts or death within one month.Cost-effectivenessCosts and quality of life. We used findings from published Community Falls Prevention Trial to model cost-effectiveness.Results17 intervention paramedics used CCDS for 54 (12.4%) of 436 participants. They referred 42 (9.6%) to falls services, compared with 17 (5.0%) of 343 participants seen by 19 control paramedics [Odds ratio (OR) 2.04, 95% CI 1.12 to 3.72]. No adverse events were related to the intervention. Non-significant differences between groups included: subsequent emergency contacts (34.6% versus 29.1%; OR 1.27, 95% CI 0.93 to 1.72); quality of life (mean SF12 differences: MCS -0.74, 95% CI -2.83 to +1.28; PCS -0.13, 95% CI -1.65 to +1.39) and non-conveyance (42.0% versus 36.7%; OR 1.13, 95% CI 0.84 to 1.52). However ambulance job cycle time was 8.9 minutes longer for intervention patients (95% CI 2.3 to 15.3). Average net cost of implementing CCDS was £208 per patient with existing electronic data capture, and £308 without. Modelling estimated cost per quality-adjusted life-year at £15,000 with existing electronic data capture; and £22,200 without.ConclusionsIntervention paramedics referred twice as many participants to falls services with no difference in safety. CCDS is potentially cost-effective, especially with existing electronic data capture.Trial registrationISRCTN Register ISRCTN10538608.

Dataset Information

Using Electronic Clinical Decision Support to Examine Vision Rehabilitation Referrals and Practice Guidelines in Ophthalmology.

Purpose

Methods

Results

Conclusions

Translational relevance

Publications

Using Electronic Clinical Decision Support to Examine Vision Rehabilitation Referrals and Practice Guidelines in Ophthalmology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets