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Comparison of 3 Surveillance Methods to Detect Potential Controlled Substance Diversion in an Academic Medical Center.

ABSTRACT: Objectives: To compare 3 methods of detecting potential diversion of controlled substances (CS) by health care personnel from inpatient units in a large, academic medical center. Methods: Three different reports were retrospectively analyzed and evaluated to determine which employees are "high-risk" for diversion over a 30-day period using defined criteria. Reports were derived from automated dispensing machines (ADMs), purchased third-party software (TPS), and the electronic health record (EHR). The primary outcome was the percentage of employees in each report who were deemed to be high-risk for CS diversion (positive predictive value [PPV]). Secondary outcomes included the number of false positives and description of high-risk users on each report. Descriptive statistics were used to analyze differences between methods. Results: The PPV was highly variable between reports. The PPVs among the ADM, TPS, and EHR reports were 3.28%, 6.82%, and 23.88%, respectively. False positives were high among all reports (96.72%, 93.18%, and 76.12% for the ADM, TPS, and EHR reports, respectively). Conclusions: A report from the EHR has the highest PPV to detect high-risk employees who may be diverting CS. However, false positives were high for all reports, indicating that significant improvements are needed in the development of accurate and reliable software to detect potential and actual CS diversion.

SUBMITTER: Derington CG

PROVIDER: S-EPMC7502867 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress. OBJECTIVE:The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. METHODS:On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. RESULTS:Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. CONCLUSIONS:The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

Project description:BackgroundAcademic Medical Centers (AMCs) must simultaneously serve different purposes: Delivery of high quality healthcare services to patients, as the main mission, supported by other core missions such as academic activities, i.e., researching, teaching and tutoring, while maintaining solvency. This study aims to develop a methodology for constructing models evaluating the academic value provided by AMCs and implementing it at the largest AMC in Israel.MethodsThirty five practiced educators and researchers, academic experts, faculty members and executives, all employed by a metropolitan 1500-bed AMC, were involved in developing academic quality indicators. First, an initial list of AMCs' academic quality indicators was drafted, using a literature review and consulting scholars. Afterwards, additional data and preferences were collected by conducting semi-structured interviews, complemented by a three-round Delphi Panel. Finally, the methodology for constructing a model evaluating the academic value provided by the AMC was developed.ResultsThe composite academic quality indicators methodology consists of nine indicators (relative weight in parentheses): 'Scientific Publications Value' (18.7%), 'Completed Studies' (13.5%), 'Authors Value' (13.0%), 'Residents Quality' (11.3%), 'Competitive Grants Budget' (10.2%), 'Academic Training' (8.7%), 'Academic Positions' (8.3%), 'Number of Studies' (8.3%) and 'Academic Supervision' (8.0%). These indicators were grouped into three core categories: 'Education', 'Research' and 'Publications', having almost the same importance on a scale from zero to one (0-1), i.e., 0.363, 0.320, and 0.317, respectively. The results demonstrated a high level of internal consistency (Cronbach-alpha range: 0.79-0.86).ConclusionsWe have found a gap in the ability to measure academic value provided by AMCs. The main contribution of this research is the development of methodology for constructing evaluation models for AMCs academic performance. Further studies are needed to further test the validity and reliability of the proposed methodology at other sites.

Project description:BACKGROUND: The first year of graduate medical education is an important period in the professional development of physicians. Disruptive behavior interferes with safe and effective clinical practice. OBJECTIVE: To determine the frequency and nature of disruptive behavior perceived by interns and attending physicians in a teaching hospital environment. METHOD: All 516 interns at Partners HealthCare (Boston, MA) during the 2010 and 2011 academic years were eligible to complete an anonymous questionnaire. A convenience nonrandom sample of 40 attending physicians also participated. RESULTS: A total of 394 of 516 eligible interns (76.4%) participated. Attendings and interns each reported that their team members generally behaved professionally (87.5% versus 80.4%, respectively). A significantly greater proportion of attendings than interns felt respected at work (90.0% versus 71.5% respectively; P = .01). Disruptive behavior was experienced by 93% of interns; 54% reported that they experienced it once a month or more. Interns reported disruptive behavior significantly more frequently than attending physicians, including increased reports of condescending behavior (odds ratio [OR], 5.46 for interns compared with attendings; P < .001), exclusion from decision making (OR, 6.97; P < .001), and berating (OR, 4.84; P = .02). Inappropriate jokes, abusive language, and gender bias were also reported, and they were not significantly more frequent among interns than attending physicians. Interns most frequently identified nurses as the source of disruption, and were significantly more likely than faculty to identify nurses as the source of disruptive behavior (OR, 10.40; P < .001). Attendings reported other physicians as the most frequent source of disruption. CONCLUSIONS: Although interns generally feel respected at work, they frequently experience disruptive behavior. Interns described more disruptive behaviors than a convenience sample of attending physicians at our institution.

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Comparison of 3 Surveillance Methods to Detect Potential Controlled Substance Diversion in an Academic Medical Center.

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