Decision Support Using Anesthesia Information Management System Records and Accreditation Council for Graduate Medical Education Case Logs for Resident Operating Room Assignments

Wanderer, Jonathan P.; Charnin, Jonathan E.; Driscoll, William; Bailin, Michael T.; Baker, Keith

doi:10.1213/ane.0b013e318294fb64

Cited by 23 publications

(16 citation statements)

References 6 publications

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“…We created a web service implementation of the RSI model for one-year mortality and incorporated it into a local clinical decision support tool for resident clinical assignments, Rhinos (Residents Helping In Navigating OR Scheduling) [25], in order to provide automated assessment of underlying patient complexity when assigning OR cases.…”

Section: Introductionmentioning

confidence: 99%

Matching Clinicians to Operative Cases

Wąs¹,

Wanderer²

2013

Appl Clin Inform

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SummaryBackground: Patient and surgical case complexity are important considerations in creating appropriate clinical assignments for trainees in the operating room (OR). The American Society of Anesthesiologists (ASA) Physical Status Classification System is the most commonly used tool to classify patient illness severity, but it requires manual evaluation by a clinician and is highly variable. A Risk Stratification System for surgical patients was recently published which uses administrative billing codes to calculate four Risk Stratification Indices (RSIs) and provides an objective surrogate for patient complexity that does not require clinical evaluation. This risk score could be helpful when assigning operating room cases. Objective: This is a technical feasibility study to evaluate the process and potential utility of incorporating an automatic risk score calculation into a web-based tool for assigning OR cases. Methods: We created a web service implementation of the RSI model for one-year mortality and automatically calculated the RSI values for patients scheduled to undergo an operation the following day. An analysis was conducted on data availability for the RSI model and the correlation between RSI values and ASA physical status. Results: In a retrospective analysis of 46,740 patients who received surgery in the year preceding the web tool implementation, RSI values were generated for 20,638 patients (44%). The Spearman's rank correlation coefficient between ASA physical status classification and one-year mortality RSI values was 0.404. Conclusions:We have shown that it is possible to create a web-based tool that uses existing billing data to automatically calculate risk scores for patients scheduled to undergo surgery. Such a risk scoring system could be used to match patient acuity to physician experience, and to provide improved patient and clinician experiences. The web tool could be improved by expanding the input database or utilizing procedure booking codes rather than billing data.

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Section: Introductionmentioning

confidence: 99%

Matching Clinicians to Operative Cases

Wąs¹,

Wanderer²

2013

Appl Clin Inform

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“…Patient attribution is the process of attributing patient-level metrics to specific providers, 1 which are signals of real-life provider-patient interactions (PPIs). Such attribution is essential for measuring provider-specific quality metrics, 2 determining physician case exposure, 3,4 and identifying accountable care team members. 5 Attributing the care of patients to providers has been attempted through a myriad of approaches.…”

Section: Background and Significancementioning

confidence: 99%

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs

et al. 2020

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Objective Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider–patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs). Methods We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow. Results We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19–278.56; ED: OR = 19.06, 95% CI: 9.53–41.65' inpatient: OR = 2.95, 95% CI: 2.23–3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). Conclusion Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.

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“…Anesthesia Information Management Systems has proven useful for maintaining trainee case logs , monitoring their experience , and for improving anesthesia exposure variability . It is possible that future improvements to AIMSs will include reference information such as drug monographs, scientific articles, clinical guidelines, and electronic textbooks, enabling trainees (and their seniors) case‐specific reference information at the bedside to support clinical decisions .…”

Section: Uses Of Datamentioning

confidence: 99%

“…AIMS‐based case log files improve accuracy and decrease the clerical burden on anesthesia trainees . The use of continuously updated records can support operating assignment and evaluate these assignments so that trainees gain satisfactory exposure (e.g., case load minimums) and case load variety during clinical attachments .…”

Section: Uses Of Datamentioning

confidence: 99%

Paperless anesthesia: uses and abuses of these data

Anderson

Merry

2015

Pediatric Anesthesia

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Demonstrably accurate records facilitate clinical decision making, improve patient safety, provide better defense against frivolous lawsuits, and enable better medical policy decisions. Anesthesia Information Management Systems (AIMS) have the potential to improve on the accuracy and reliability of handwritten records. Interfaces with electronic recording systems within the hospital or wider community allow correlation of anesthesia relevant data with biochemistry laboratory results, billing sections, radiological units, pharmacy, earlier patient records, and other systems. Electronic storage of large and accurate datasets has lent itself to quality assurance, enhancement of patient safety, research, cost containment, scheduling, anesthesia training initiatives, and has even stimulated organizational change. The time for record making may be increased by AIMS, but in some cases has been reduced. The question of impact on vigilance is not entirely settled, but substantial negative effects seem to be unlikely. The usefulness of these large databases depends on the accuracy of data and they may be incorrect or incomplete. Consequent biases are threats to the validity of research results. Data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate misleading research findings for the purpose of manipulating public opinion and swaying policymakers. There remains a fear that accessibility of data may have undesirable regulatory or legal consequences. Increasing regulation of treatment options during the perioperative period through regulated policies could reduce autonomy for clinicians. These fears are as yet unsubstantiated.

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Decision Support Using Anesthesia Information Management System Records and Accreditation Council for Graduate Medical Education Case Logs for Resident Operating Room Assignments

Cited by 23 publications

References 6 publications

Matching Clinicians to Operative Cases

Matching Clinicians to Operative Cases

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs

Paperless anesthesia: uses and abuses of these data

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