Identifying End Users' Preferences about Structuring Pharmacogenetic Test Orders in an Electronic Health Record System.
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SUBMITTER: Hull LE
PROVIDER: S-EPMC7527867 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine-learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient's discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty-five percent of medications ordered by physicians were ranked in the sequence model's top-10 predictions (logistic model: 49%) and 75% ranked in the top-25 (logistic model: 69%). Ninety-three percent of the sequence model's top-10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR.
| S-EPMC7325318 | biostudies-literature
Project description:BackgroundThe rapid expansion in the use of electronic health records (EHR) has increased the number of medical errors originating in health information systems (HIS). The sociotechnical approach helps in understanding risks in the development, implementation, and use of EHR and health information technology (HIT) while accounting for complex interactions of technology within the health care system.ObjectiveThis study addresses two important questions: (1) "which of the common EHR error types are associated with perceived high- and extreme-risk severity ratings among EHR users?", and (2) "which variables are associated with high- and extreme-risk severity ratings?"MethodsThis study was a quantitative, non-experimental, descriptive study of EHR users. We conducted a cross-sectional web-based questionnaire study at the largest hospital district in Finland. Statistical tests included the reliability of the summative scales tested with Cronbach's alpha. Logistic regression served to assess the association of the independent variables to each of the eight risk factors examined.ResultsA total of 2864 eligible respondents provided the final data. Almost half of the respondents reported a high level of risk related to the error type "extended EHR unavailability". The lowest overall risk level was associated with "selecting incorrectly from a list of items". In multivariate analyses, profession and clinical unit proved to be the strongest predictors for high perceived risk. Physicians perceived risk levels to be the highest ( P<.001 in six of eight error types), while emergency departments, operating rooms, and procedure units were associated with higher perceived risk levels ( P<.001 in four of eight error types). Previous participation in eLearning courses on EHR-use was associated with lower risk for some of the risk factors.ConclusionsBased on a large number of Finnish EHR users in hospitals, this study indicates that HIT safety hazards should be taken very seriously, particularly in operating rooms, procedure units, emergency departments, and intensive care units/critical care units. Health care organizations should use proactive and systematic assessments of EHR risks before harmful events occur. An EHR training program should be compulsory for all EHR users in order to address EHR safety concerns resulting from the failure to use HIT appropriately.
| S-EPMC4890731 | biostudies-literature
Project description:We validated an algorithm designed to identify new or prevalent users of antidepressant medications via population-based drug prescription records.We obtained population-based drug prescription records for the entire Olmsted County, Minnesota, population from 2011 to 2012 (N=149,629) using the existing electronic medical records linkage infrastructure of the Rochester Epidemiology Project (REP). We selected electronically a random sample of 200 new antidepressant users stratified by age and sex. The algorithm required the exclusion of antidepressant use in the 6 months preceding the date of the first qualifying antidepressant prescription (index date). Medical records were manually reviewed and adjudicated to calculate the positive predictive value (PPV). We also manually reviewed the records of a random sample of 200 antihistamine users who did not meet the case definition of new antidepressant user to estimate the negative predictive value (NPV).161 of the 198 subjects electronically identified as new antidepressant users were confirmed by manual record review (PPV 81.3%). Restricting the definition of new users to subjects who were prescribed typical starting doses of each agent for treating major depression in non-geriatric adults resulted in an increase in the PPV (90.9%). Extending the time windows with no antidepressant use preceding the index date resulted in only modest increases in PPV. The manual abstraction of medical records of 200 antihistamine users yielded an NPV of 98.5%.Our study confirms that REP prescription records can be used to identify prevalent and incident users of antidepressants in the Olmsted County, Minnesota, population.
| S-EPMC4147111 | biostudies-literature
Project description:ObjectiveThe goal of this investigation was to determine whether automated approaches can learn patient-oriented care teams via utilization of an electronic medical record (EMR) system.Materials and methodsTo perform this investigation, we designed a data-mining framework that relies on a combination of latent topic modeling and network analysis to infer patterns of collaborative teams. We applied the framework to the EMR utilization records of over 10 000 employees and 17 000 inpatients at a large academic medical center during a 4-month window in 2010. Next, we conducted an extrinsic evaluation of the patterns to determine the plausibility of the inferred care teams via surveys with knowledgeable experts. Finally, we conducted an intrinsic evaluation to contextualize each team in terms of collaboration strength (via a cluster coefficient) and clinical credibility (via associations between teams and patient comorbidities).ResultsThe framework discovered 34 collaborative care teams, 27 (79.4%) of which were confirmed as administratively plausible. Of those, 26 teams depicted strong collaborations, with a cluster coefficient > 0.5. There were 119 diagnostic conditions associated with 34 care teams. Additionally, to provide clarity on how the survey respondents arrived at their determinations, we worked with several oncologists to develop an illustrative example of how a certain team functions in cancer care.DiscussionInferred collaborative teams are plausible; translating such patterns into optimized collaborative care will require administrative review and integration with management practices.ConclusionsEMR utilization records can be mined for collaborative care patterns in large complex medical centers.
| S-EPMC6080725 | biostudies-literature
Project description:ObjectivesTo create test collections for evaluating clinical information retrieval (IR) systems and advancing clinical IR research.Materials and methodsElectronic health record (EHR) data, including structured and free-text data, from 45 000 patients who are a part of the Mayo Clinic Biobank cohort was retrieved from the clinical data warehouse. The clinical IR system indexed a total of 42 million free-text EHR documents. The search queries consisted of 56 topics developed through a collaboration between Mayo Clinic and Oregon Health & Science University. We described the creation of test collections, including a to-be-evaluated document pool using five retrieval models, and human assessment guidelines. We analyzed the relevance judgment results in terms of human agreement and time spent, and results of three levels of relevance, and reported performance of five retrieval models.ResultsThe two judges had a moderate overall agreement with a Kappa value of 0.49, spent a consistent amount of time judging the relevance, and were able to identify easy and difficult topics. The conventional retrieval model performed best on most topics while a concept-based retrieval model had better performance on the topics requiring conceptual level retrieval.DiscussionIR can provide an alternate approach to leveraging clinical narratives for patient information discovery as it is less dependent on semantics. Our study showed the feasibility of test collections along with a few challenges.ConclusionThe conventional test collections for evaluating the IR system show potential for successfully evaluating clinical IR systems with a few challenges to be investigated.
| S-EPMC6824517 | biostudies-literature
Project description:Background: While electronic cigarette (ECIG) use is rapidly rising, their safety profile remains uncertain. The effects of tobacco cigarette (TCIG) smoke on bronchial airway epithelial gene-expression have provided insights into tobacco-related disease pathogenesis. Understanding the impact of electronic cigarettes (ECIGs) on airway gene-expression could provide insights into their potential long-term health effects. Objectives: We sought to compare the bronchial airway gene-expression profiles of former TCIG smokers now using ECIGs with the profiles of former and current TCIG smokers. Methods: We performed gene-expression profiling of bronchial epithelial cells collected from TCIG smokers not using ECIGs (n=21), former smokers using ECIGs (n=15), and current TCIG smokers not using ECIGs (n=9). We then compared our findings with previous studies of the effects of TCIG use on bronchial epithelium, as well an in vitro model of ECIG exposure. Results: Amongst 3,165 genes whose expression varied between the three study groups (q < 0.05), we identified 468 genes significantly altered in ECIG users relative to former smokers (p < 0.05). 79 of these genes were up or down-regulated concordantly between ECIG and TCIG. We did not detect ECIG-associated gene expression changes in known pathways associated with TCIG usage. Genes downregulated in ECIG users are enriched among the genes most downregulated by exposure of airway epithelium to ECIG vapor in vitro. Conclusions: TCIG exposure was associated with a larger number of airway gene-expression changes than with ECIG exposures. ECIGs induce both distinct and shared patterns of gene expression relative to TCIGs in the bronchial airway epithelium.
2019-10-09 | GSE112073 | GEO
Project description:Recent studies suggested that insulin glargine use could be associated with increased risk of cancer. We compared the incidence of cancer in new users of glargine versus new users of NPH in a longitudinal clinical cohort with diabetes for up to 6 years.From all patients who had been regularly followed at Massachusetts General Hospital from 1/01/2005 to 12/31/2010, 3,680 patients who had a medication record for glargine or NPH usage were obtained from the electronic medical record (EMR). From those we selected 539 new glargine users (age: 60.1±13.6 years, BMI: 32.7±7.5 kg/m2) and 343 new NPH users (61.5±14.1 years, 32.7±8.3 kg/m2) who had no prevalent cancer during 19 months prior to glargine or NPH initiation. All incident cancer cases were ascertained from the EMR requiring at least 2 ICD-9 codes within a 2 month period. Insulin exposure time and cumulative dose were validated. The statistical analysis compared the rates of cancer in new glargine vs. new NPH users while on treatment, adjusted for the propensity to receive one or the other insulin. There were 26 and 28 new cancer cases in new glargine and new NPH users for 1559 and 1126 person-years follow-up, respectively. There were no differences in the propensity-adjusted clinical characteristics between groups. The adjusted hazard ratio for the cancer incidence comparing glargine vs. NPH use was 0.65 (95% CI: 0.36-1.19).Insulin glargine is not associated with development of cancers when compared with NPH in this longitudinal and carefully retrieved EMR data.
| S-EPMC4203726 | biostudies-literature
Project description:BackgroundElectronic health record (EHR) patient portals are designed to provide medical health records to patients. Using an EHR portal is expected to contribute to positive health outcomes and facilitate patient-provider communication.ObjectiveOur objective was to examine how portal users report using their portals and the factors associated with obtaining health information from the internet. We also examined the desired portal features, factors impacting users' trust in portals, and barriers to using portals.MethodsAn internet-based survey study was conducted using Amazon Mechanical Turk. All the participants were adults in the United States who used patient portals. The survey included questions about how the participants used their portals, what factors acted as barriers to using their portals, and how they used and how much they trusted other web-based health information sources as well as their portals. A logistic regression model was used to examine the factors influencing the participants' trust in their portals. Additionally, the desired features and design characteristics were identified to support the design of future portals.ResultsA total of 394 participants completed the survey. Most of the participants were less than 35 years old (212/394, 53.8%), with 36.3% (143/394) aged between 35 and 55 years, and 9.9% (39/394) aged above 55 years. Women accounted for 48.5% (191/394) of the survey participants. More than 78% (307/394) of the participants reported using portals at least monthly. The most common portal features used were viewing lab results, making appointments, and paying bills. Participants reported some barriers to portal use including data security and limited access to the internet. The results of a logistic regression model used to predict the trust in their portals suggest that those comfortable using their portals (odds ratio [OR] 7.97, 95% CI 1.11-57.32) thought that their portals were easy to use (OR 7.4, 95% CI 1.12-48.84), and frequent internet users (OR 43.72, 95% CI 1.83-1046.43) were more likely to trust their portals. Participants reporting that the portals were important in managing their health (OR 28.13, 95% CI 5.31-148.85) and that their portals were a valuable part of their health care (OR 6.75, 95% CI 1.51-30.11) were also more likely to trust their portals.ConclusionsThere are several factors that impact the trust of EHR patient portal users in their portals. Designing easily usable portals and considering these factors may be the most effective approach to improving trust in patient portals. The desired features and usability of portals are critical factors that contribute to users' trust in EHR portals.
| S-EPMC8493465 | biostudies-literature
Project description:BackgroundAdverse drug reactions (ADRs) are estimated to be the fifth cause of hospital death. Up to 50% are potentially preventable and a significant number are recurrent (reADRs). Clinical decision support systems have been used to prevent reADRs using structured reporting concerning the patient's ADR experience, which in current clinical practice is poorly performed. Identifying ADRs directly from free text in electronic health records (EHRs) could circumvent this.AimTo develop strategies to identify ADRs from free-text notes in electronic hospital health records.MethodsIn stage I, the EHRs of 10 patients were reviewed to establish strategies for identifying ADRs. In stage II, complete EHR histories of 45 patients were reviewed for ADRs and compared to the strategies programmed into a rule-based model. ADRs were classified using MedDRA and included in the study if the Naranjo causality score was ≥1. Seriousness was assessed using the European Medicine Agency's important medical event list.ResultsIn stage I, two main search strategies were identified: keywords indicating an ADR and specific prepositions followed by medication names. In stage II, the EHRs contained a median of 7.4 (range 0.01-18) years of medical history covering over 35 000 notes. A total of 318 unique ADRs were identified of which 63 were potentially serious and 179 (sensitivity 57%) were identified by the rule. The method falsely identified 377 ADRs (positive predictive value 32%). However, it also identified an additional eight ADRs.ConclusionTwo key strategies were developed to identify ADRs from hospital EHRs using free-text notes. The results appear promising and warrant further study.
| S-EPMC9292762 | biostudies-literature
Project description:Usage log data are an important data source for characterizing the potential burden related to use of the electronic health record (EHR) system. However, the utility of this data source has been hindered by concerns related to the real-world validity and accuracy of the data. While time-motion studies have historically been used to address this concern, the restrictions caused by the pandemic have made it difficult to carry out these studies in-person. In this regard, we introduce a practical approach for conducting validation studies for usage log data in a controlled environment. By developing test runs based on clinical workflows and conducting them within a test EHR environment, it allows for both comparison of the recorded timings and retrospective investigation of any discrepancies. In this case report, we describe the utility of this approach for validating our physician EHR usage logs at a large academic teaching mental health hospital in Canada. A total of 10 test runs were conducted across 3 days to validate 8 EHR usage log metrics, finding differences between recorded measurements and the usage analytics platform ranging from 9 to 60%.
| S-EPMC9534596 | biostudies-literature