Constructing data-derived family histories using electronic health records from a single healthcare delivery system.
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ABSTRACT:
SUBMITTER: Leventer-Roberts M
PROVIDER: S-EPMC7183366 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND Predicting recurrent Clostridium difficile infection (rCDI) remains difficult.We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007-2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model. RESULTS Despite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591-0.605), had good calibration, or had good explanatory power. CONCLUSIONS Our ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power. Infect Control Hosp Epidemiol 2017;38:1196-1203.
| S-EPMC6008100 | biostudies-literature
Project description:BackgroundDrug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings.ObjectiveTo describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network.MethodsPost-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible.ResultsOverall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate.LimitationsAlthough global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out.ConclusionA strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation.
| S-EPMC3756064 | biostudies-literature
Project description:electronic Medical Records & Genomics (eMERGE)
| PRJNA158491 | ENA
Project description:BackgroundReporting of strategic healthcare-associated infections (HCAIs) to Public Health England is mandatory for all acute hospital trusts in England, via a web-based HCAI Data Capture System (HCAI-DCS).AimInvestigate the feasibility of automating the current, manual, HCAI reporting using linked electronic health records (linked-EHR), and assess its level of accuracy.MethodsAll data previously submitted through the HCAI-DCS by the Oxford University Hospitals infection control (IC) team for methicillin-resistant and methicillin-susceptible Staphylococcus aureus (MRSA, MSSA), Clostridium difficile, and Escherichia coli, through March 2017 were downloaded and compared to outputs created from linked-EHR, with detailed comparisons between 2013-2017.FindingsTotal MRSA, MSSA, E. coli and C. difficile cases entered by the IC team vs linked-EHR were 428 vs 432, 795 vs 816, 2454 vs 2450 and 3365 vs 3393 respectively. From 2013-2017, most discrepancies (32/37 (86%)) were likely due to IC recording errors. Patient and specimen identifiers were completed for >98% of cases by both methods, with very high agreement (>97%). Fields relating to the patient at the time the specimen was taken were complete to a similarly high level (>99% IC, >97% linked-EHR), and agreement was fairly good (>80%) except for the main and treatment specialties (57% and 54% respectively) and the patient category (55%). Optional, organism-specific data-fields were less complete, by both methods. Where comparisons were possible, agreement was reasonably high (mostly 70-90%).ConclusionBasic factual information, such as demographic data, is almost-certainly better automated, and many other data fields can potentially be populated successfully from linked-EHR. Manual data collection is time-consuming and inefficient; automated electronic data collection would leave healthcare professionals free to focus on clinical rather than administrative work.
| S-EPMC6221334 | biostudies-literature
Project description:IntroductionThe identification of population-level healthcare needs using hospital electronic medical records (EMRs) is a promising approach for the evaluation and development of tailored healthcare services. Population segmentation based on healthcare needs may be possible using information on health and social service needs from EMRs. However, it is currently unknown if EMRs from restructured hospitals in Singapore provide information of sufficient quality for this purpose. We compared the inter-rater reliability between a population segment that was assigned prospectively and one that was assigned retrospectively based on EMR review.Methods200 non-critical patients aged ≥ 55 years were prospectively evaluated by clinicians for their healthcare needs in the emergency department at Singapore General Hospital, Singapore. Trained clinician raters with no prior knowledge of these patients subsequently accessed the EMR up to the prospective rating date. A similar healthcare needs evaluation was conducted using the EMR. The inter-rater reliability between the two rating sets was evaluated using Cohen's Kappa and the incidence of missing information was tabulated.ResultsThe inter-rater reliability for the medical 'global impression' rating was 0.37 for doctors and 0.35 for nurses. The inter-rater reliability for the same variable, retrospectively rated by two doctors, was 0.75. Variables with a higher incidence of missing EMR information such as 'social support in case of need' and 'patient activation' had poorer inter-rater reliability.ConclusionPre-existing EMR systems may not capture sufficient information for reliable determination of healthcare needs. Thus, we should consider integrating policy-relevant healthcare need variables into EMRs.
| S-EPMC7911077 | biostudies-literature
Project description:Use of electronic nicotine delivery systems (ENDS) has increased substantially over the past decade. However, unlike smoking, which is systematically captured by clinicians through routine screening and discrete documentation fields in the electronic health record (EHR), unknown is the extent to which clinicians are documenting patients' use of ENDS. Data were gathered from medical visits with patients aged 12 and older (N=9,119; 55% male) treated in a large, integrated healthcare system. We used natural language processing to assess the incidence rates of clinician documentation of patients' ENDS use in unstructured tobacco comments in the EHR, and the words most frequently documented in relation to ENDS, from 2006-2015. ENDS documentation in the EHR increased dramatically over time (from 0.01 to 9.5 per 10,000 patients, p<0.0001), particularly among adults aged 18-24 and 25-44. Most prevalent were "e-cig," "electronic cigarettes", and "vape," with much variation in spelling and phrasing of these words. Records of adolescent and young adult patients were more likely to contain the word "vape", and less likely to have "e-cig" and "electronic cigarette" than records of adults (ps<0.0001). The relatively low observed number of patients with ENDS terms in the EHR suggested vast under documentation. While healthcare providers are increasingly documenting patients' use of ENDS in the EHR, overall documentation rates remain low. Discrete EHR fields for standard screening and documentation of ENDS that reflect the language used by patients would provide more complete longitudinal population-level surveillance of ENDS use and its association with short- and long-term health outcomes.
| S-EPMC5796533 | biostudies-literature
Project description:BackgroundApplication of Artificial Intelligence (AI) and the use of agent-based systems in the healthcare system have attracted various researchers to improve the efficiency and utility in the Electronic Health Records (EHR). Nowadays, one of the most important and creative developments is the integration of AI and Blockchain that is, Distributed Ledger Technology (DLT) to enable better and decentralized governance. Privacy and security is a critical piece in EHR implementation and/or adoption. Health records are updated every time a patient visits a doctor as they contain important information about the health and wellbeing of the patient and describes the history of care received during the past and to date. Therefore, such records are critical to research, hospitals, emergency rooms, healthcare laboratories, and even health insurance providers.MethodsIn this article, a platform employing the AI and the use of multi-agent based systems along with the DLT technology for privacy preservation is proposed. The emphasis of security and privacy is highlighted during the process of collecting, managing and distributing EHR data.ResultsThis article aims to ensure privacy, integrity and security metrics of the electronic health records are met when such copies are not only immutable but also distributed. The findings of this work will help guide the development of further techniques using the combination of AI and multi-agent based systems backed by DLT technology for secure and effective handling EHR data. This proposed architecture uses various AI-based intelligent based agents and blockchain for providing privacy and security in EHR. Future enhancement in this work can be the addition of the biometric based systems for improved security.
| S-EPMC7924429 | biostudies-literature
Project description:BackgroundLinkage of electronic healthcare records is becoming increasingly important for research purposes. However, linkage error due to mis-recorded or missing identifiers can lead to biased results. We evaluated the impact of linkage error on estimated infection rates using two different methods for classifying links: highest-weight (HW) classification using probabilistic match weights and prior-informed imputation (PII) using match probabilities.MethodsA gold-standard dataset was created through deterministic linkage of unique identifiers in admission data from two hospitals and infection data recorded at the hospital laboratories (original data). Unique identifiers were then removed and data were re-linked by date of birth, sex and Soundex using two classification methods: i) HW classification - accepting the candidate record with the highest weight exceeding a threshold and ii) PII-imputing values from a match probability distribution. To evaluate methods for linking data with different error rates, non-random error and different match rates, we generated simulation data. Each set of simulated files was linked using both classification methods. Infection rates in the linked data were compared with those in the gold-standard data.ResultsIn the original gold-standard data, 1496/20924 admissions linked to an infection. In the linked original data, PII provided least biased results: 1481 and 1457 infections (upper/lower thresholds) compared with 1316 and 1287 (HW upper/lower thresholds). In the simulated data, substantial bias (up to 112%) was introduced when linkage error varied by hospital. Bias was also greater when the match rate was low or the identifier error rate was high and in these cases, PII performed better than HW classification at reducing bias due to false-matches.ConclusionsThis study highlights the importance of evaluating the potential impact of linkage error on results. PII can help incorporate linkage uncertainty into analysis and reduce bias due to linkage error, without requiring identifiers.
| S-EPMC4015706 | biostudies-literature
Project description:To explore physician satisfaction with an electronic medical records (EMR) system, to identify and explore the main limitations of the system and finally to submit recommendations to address these limitations.A descriptive qualitative study that entailed three focus group interviews was performed among physicians using open-ended questions. The interviews were audiotaped, documented and transcribed verbatim. The themes were explored and analysed in different categories.The study was conducted in primary healthcare centres (PHC) in Al Ain, United Arab Emirates (UAE).A total of 23 physicians, all using the same EMR system, attended one of three focus groups held in PHC in Al Ain Medical District. Each focus group consisted of 7-9 physicians working in PHC as family medicine specialists, residents or general practitioners.Physician satisfaction with the EMR system.Key themes emerged and were categorised as physician-dependent, patient-related and system-related factors. In general, physicians were satisfied with the EMR system in spite of initial difficulties with implementation. Most participants identified that the long time required to do the documentation affected their practice and patient communication. Many physicians expressed satisfaction with the orders and results of laboratory and radiology functions and they emphasised that this was the strongest point in the EMR. They were also satisfied with the electronic prescription function, stating that it reduced errors and saved time.Physicians are satisfied with the EMR and have a positive perception regarding the application of the system. Several themes emerged during this study that need to be considered to enhance the EMR system. Further studies need to be conducted among other healthcare practitioners and patients to explore their attitude and perception about the EMR.
| S-EPMC4225459 | biostudies-literature
Project description:Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data sources is essential to enable better detection, strengthening and validation of signals. In this study, we investigated the potential of electronic healthcare records (EHRs) to be used alongside an SRS as an independent system, with the aim of improving signal detection.A signal detection strategy, focused on a limited set of adverse events deemed important in pharmacovigilance, was performed retrospectively in two data sources-(1) the Exploring and Understanding Adverse Drug Reactions (EU-ADR) database network and (2) the EudraVigilance database-using data between 2000 and 2010. Five events were considered for analysis: (1) acute myocardial infarction (AMI); (2) bullous eruption; (3) hip fracture; (4) acute pancreatitis; and (5) upper gastrointestinal bleeding (UGIB). Potential signals identified in each system were verified using the current published literature. The complementarity of the two systems to detect signals was expressed as the percentage of the unilaterally identified signals out of the total number of confirmed signals. As a proxy for the associated costs, the number of signals that needed to be reviewed to detect one true signal (number needed to detect [NND]) was calculated. The relationship between the background frequency of the events and the capability of each system to detect signals was also investigated.The contribution of each system to signal detection appeared to be correlated with the background incidence of the events, being directly proportional to the incidence in EU-ADR and inversely proportional in EudraVigilance. EudraVigilance was particularly valuable in identifying bullous eruption and acute pancreatitis (71 and 42 % of signals were correctly identified from the total pool of known associations, respectively), while EU-ADR was most useful in identifying hip fractures (60 %). Both systems contributed reasonably well to identification of signals related to UGIB (45 % in EudraVigilance, 40 % in EU-ADR) but only fairly for signals related to AMI (25 % in EU-ADR, 20 % in EudraVigilance). The costs associated with detection of signals were variable across events; however, it was often more costly to detect safety signals in EU-ADR than in EudraVigilance (median NNDs: 7 versus 5).An EHR-based system may have additional value for signal detection, alongside already established systems, especially in the presence of adverse events with a high background incidence. While the SRS appeared to be more cost effective overall, for some events the costs associated with signal detection in the EHR might be justifiable.
| S-EPMC4659852 | biostudies-other