Development of novel composite data quality scores to evaluate facility-level data quality in electronic data in Kenya: a nationwide retrospective cohort study.

BACKGROUND: In this evaluation, we aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. We leverage electronic data from the Kenya Health Information System (KHIS) which is based on the District Health Information System version 2 (DHIS2) to perform DQAs at scale. We provide a systematic guide to developing composite data quality scores and use these scores to assess data quality in Kenya. METHODS: We evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), we extracted indicators encompassing general HIV services including services to prevent mother-to-child transmission (PMTCT). We assessed the accuracy (the extent to which data were correct and free of error) of these data using three data-driven composite scores: 1) completeness score; 2) consistency score; and 3) discrepancy score. Completeness refers to the presence of the appropriate amount of data. Consistency refers to uniformity of data across multiple indicators. Discrepancy (measured on a Z-scale) refers to the degree of alignment (or lack thereof) of data with rules that defined the possible valid values for the data. RESULTS: A total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean discrepancy score was 0.07 (SD = 0.22). A strong and positive correlation was identified between the consistency score and discrepancy score (correlation coefficient = 0.77), whereas the correlation of either score with the completeness score was low with a correlation coefficient of -0.12 (with consistency score) and -0.36 (with discrepancy score). General HIV indicators were more complete, but less consistent, and less plausible than PMTCT indicators. CONCLUSION: We observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consistency or discrepancy to reflect distinct dimensions of data quality. Given the complexity of the discrepancy score, we recommend the simpler consistency score, since they were highly correlated. Routine use of composite scores on KHIS data could enhance efficiencies in DQA at scale as digitization of health information expands and could be applied to other health sectors beyondHIV clinics.

Users' perception on factors contributing to electronic medical records systems use: a focus group discussion study in healthcare facilities setting in Kenya.

BACKGROUND: Electronic medical records systems (EMRs) adoption in healthcare to facilitate work processes have become common in many countries. Although EMRs are associated with quality patient care, patient safety, and cost reduction, their adoption rates are comparatively low. Understanding factors associated with the use of the implemented EMRs are critical for advancing successful implementations and scale-up sustainable initiatives. The aim of this study was to explore end users' perceptions and experiences on factors facilitating and hindering EMRs use in healthcare facilities in Kenya, a low- and middle-income country. METHODS: Two focus group discussions were conducted with EMRs users (n = 20) each representing a healthcare facility determined by the performance of the EMRs implementation. Content analysis was performed on the transcribed data and relevant themes derived. RESULTS: Six thematic categories for both facilitators and barriers emerged, and these related to (1) system functionalities; (2) training; (3) technical support; (4) human factors; (5) infrastructure, and (6) EMRs operation mode. The identified facilitators included: easiness of use and learning of the system complemented by EMRs upgrades, efficiency of EMRs in patient data management, responsive information technology (IT) and collegial support, and user training. The identified barriers included: frequent power blackouts, inadequate computers, retrospective data entry EMRs operation mode, lack of continuous training on system upgrades, and delayed IT support. CONCLUSIONS: Users generally believed that the EMRs improved the work process, with multiple factors identified as facilitators and barriers to their use. Most users perceived system functionalities and training as motivators to EMRs use, while infrastructural issues posed as the greatest barrier. No specific EMRs use facilitators and/or barriers could be attributed to facility performance levels. Continuous evaluations are necessary to assess improvements of the identified factors as well as determine emerging issues.

Open Access as a Revolution: Knowledge Alters Power.

The slogan "Gimme My Damn Data" has become a hallmark of a patient movement whose goal is to gain access to data in their medical records. Its first conference appearance was ten years ago, in September 2009. In the decade since there have been enormous changes in both the technology and sociology of medicine as well as in their synthesis. As the patient movement has made strides, it has been met with opposition and obstacles. It has also become clear that the availability of Open Access information is just as empowering (or disabling) as access to electronic medical records and device data. Knowledge truly is power, and to withhold knowledge is to disempower patients. This essay lays out many examples of how this shows up as we strive for the best future of care.

Needs assessment of emergency medical and rescue services in Abuja/Nigeria and environs.

BACKGROUND: Nigeria is ranked second highest in the rate of road accidents and other emergencies (Deaths, disabilities) among 193 countries of the world. There is therefore the need for analyzing Emergency Medical Rescue Services (EMRS) in the country to identify options for improvement. METHOD: The study was conducted from February, 2016 to March, 2017 in three EMRS organizations (FRSC, NEMA and MAITAMA Hospital) located in Abuja. The structure, resources, process of EMRS activities and outcome (delay times, case fatality as well as victims and service-providers satisfaction with services) were assessed through observation, time measurements and interviews. RESULTS: FRSC and NEMA offers (Road Traffic Injury) RTI and Disaster services, the ambulances consist of Intensive Care Unit(ICU) buses, Helicopters, Speed boats, motorbikes and other specialized vehicles. Mortality and morbidity recorded for 2016 was 1.1 and 2% respectively. MAITAMA is a specialist centre that offers general medical services. A total number 1227(88.8%) lives were saved during the observational period by three organizations, 60(4.9%) deaths, 132 (9.6%) disabilities, 793 (57.2%) NCDs and 593(42.8%) RTI. CONCLUSION: Non-communicable diseases (NCDs) cause many deaths and morbidities in the developing world compared to infectious diseases. There is need for total revamping and education of EMRS institutions in Nigeria and Low- Middle Income Countries (LMICs). Abuja and its surroundings suffers from delays in rapid emergency services, lack of adequate awareness, functional ambulances, minimal specialists and inadequate consumables lead to the loss of many lives.

Acute Kidney Injury Incidence in Noncritically Ill Hospitalized Children, Adolescents, and Young Adults: A Retrospective Observational Study.

BACKGROUND: Acute kidney injury (AKI) has been characterized in high-risk pediatric hospital inpatients, in whom AKI is frequent and associated with increased mortality, morbidity, and length of stay. The incidence of AKI among patients not requiring intensive care is unknown. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: 13,914 noncritical admissions during 2011 and 2012 at our tertiary referral pediatric hospital were evaluated. Patients younger than 28 days or older than 21 years of age or with chronic kidney disease (CKD) were excluded. Admissions with 2 or more serum creatinine measurements were evaluated. FACTORS: Demographic features, laboratory measurements, medication exposures, and length of stay. OUTCOME: AKI defined as increased serum creatinine level in accordance with KDIGO (Kidney Disease: Improving Global Outcomes) criteria. Based on time of admission, time interval requirements were met in 97% of cases, but KDIGO time window criteria were not strictly enforced to allow implementation using clinically obtained data. RESULTS: 2 or more creatinine measurements (one baseline before or during admission and a second during admission) in 2,374 of 13,914 (17%) patients allowed for AKI evaluation. A serum creatinine difference ≥0.3mg/dL or ≥1.5 times baseline was seen in 722 of 2,374 (30%) patients. A minimum of 5% of all noncritical inpatients without CKD in pediatric wards have an episode of AKI during routine hospital admission. LIMITATIONS: Urine output, glomerular filtration rate, and time interval criteria for AKI were not applied secondary to study design and available data. The evaluated cohort was restricted to patients with 2 or more clinically obtained serum creatinine measurements, and baseline creatinine level may have been measured after the AKI episode. CONCLUSIONS: AKI occurs in at least 5% of all noncritically ill hospitalized children, adolescents, and young adults without known CKD. Physicians should increase their awareness of AKI and improve surveillance strategies with serum creatinine measurements in this population so that exacerbating factors such as nephrotoxic medication exposures may be modified as indicated.

Using clinical decision support as a means of implementing a universal postpartum depression screening program.

A major barrier to the diagnosis of postpartum depression (PPD) includes symptom detection. The lack of awareness and understanding of PPD among new mothers, the variability in clinical presentation, and the various diagnostic strategies can increase this further. The purpose of this study was to test the feasibility of adding clinical decision support (CDS) to the electronic health record (EHR) as a means of implementing a universal standardized PPD screening program within a large, at high risk, population. All women returning to the Mount Sinai Hospital OB/GYN Ambulatory Practice for postpartum care between 2010 and 2013 were presented with the Edinburgh Postnatal Depression Scale (EPDS) in response to a CDS "hard stop" built into the EHR. Of the 2102 women who presented for postpartum care, 2092 women (99.5 %) were screened for PPD in response to a CDS hard stop module. Screens were missing on ten records (0.5 %) secondary to refusal, language barrier, or lack of clarity in the EHR. Technology is becoming increasingly important in addressing the challenges faced by health care providers. While the identification of PPD has become the recent focus of public health concerns secondary to the significant social burden, numerous barriers to screening still exist within the clinical setting. The utility of adding CDS in the form of a hard stop, requiring clinicians to enter a standardized PPD mood assessment score to the patient EHR, offers a sufficient way to address a primary barrier to PPD symptom identification at the practitioner level.

Clinicians' Attitudes Toward Electronic Health Records in Saudi Arabia.

AIM:  This study explored physicians' and nurses' attitudes toward an electronic health record (EHR) system and examined the features and factors that clinicians associated with the implementation of EHR systems. METHODS:  A self-administered anonymous questionnaire with high reliability and validity was adopted from existing research to gather clinicians' attitudes toward the EHR system implemented at King Khalid University Hospital, one of the biggest hospitals in Riyadh, Saudi Arabia. RESULTS:  A total of 438 questionnaire responses were received from the participants; 240 of them were physicians and 198 were nurses. The participants had a mean age of 43.7 years (standard deviation (SD) 17.1), 213 (52.7%) were female and 207 (47.3%) were male. Most participants (424, 96.8%) had one or more years of experience using computers, and a majority (304, 69.4%) had one or more years of experience using EHR systems. Most physicians and nurses (214, 89.5% vs. 174, 87.9%) were satisfied with their hospital's EHR system and felt that the system was highly usable and had the potential to improve communication between staff, facilitate easy storage of and access to information and lead to improved health outcomes for patients. The study found positive attitudes among clinicians concerning the quality of training and education around the new system (178, 74.2% of physicians vs. 142, 71.7% of nurses; p > 0.05) and toward leadership during the transition to HER (222, 92.5% vs. 183, 92.4%). On the other hand, a majority of nurses reported that the EHR system took longer to use and increased their workload compared with the previous analogue system (115 (47.9%) vs. 133 (67.2%); p ≤ 0.01 and 46.7% vs. 112 (64.1%)). A large majority of physicians and nurses surveyed (214 (89.2%) vs. 167 (84.3%)) stated that clinicians should be consulted in the design of such systems as a way to maximise the potential benefits of EHR and mitigate extra workload demands. CONCLUSION:  Most clinicians expressed overall satisfaction with the EHR system, but there were some areas of dissatisfaction among the respondents, such as increasing workload and stress among nurses. There is scope for further research to continue to explore physicians' and nurses' attitudes toward EHRs and for future experimental studies that examine the impact of EHRs on clinician workloads, patient health outcomes and quality of care.

A Novel Electronic Record System for Documentation and Efficient Workflow for Community Health Workers: Development and Usability Study.

BACKGROUND: The COVID-19 pandemic added to the decades of evidence that public health institutions are routinely stretched beyond their capacity. Community health workers (CHWs) can be a crucial extension of public health resources to address health inequities, but systems to document CHW efforts are often fragmented and prone to unneeded redundancy, errors, and inefficiency. OBJECTIVE: We sought to develop a more efficient data collection system for recording the wide range of community-based efforts performed by CHWs. METHODS: The Communities Organizing to Promote Equity (COPE) project is an initiative to address health disparities across Kansas, in part, through the deployment of CHWs. Our team iteratively designed and refined the features of a novel data collection system for CHWs. Pilot tests with CHWs occurred over several months to ensure that the functionality supported their daily use. Following implementation of the database, procedures were set to sustain the collection of feedback from CHWs, community partners, and organizations with similar systems to continually modify the database to meet the needs of users. A continuous quality improvement process was conducted monthly to evaluate CHW performance; feedback was exchanged at team and individual levels regarding the continuous quality improvement results and opportunities for improvement. Further, a 15-item feedback survey was distributed to all 33 COPE CHWs and supervisors for assessing the feasibility of database features, accessibility, and overall satisfaction. RESULTS: At launch, the database had 60 active users in 20 counties. Documented client interactions begin with needs assessments (modified versions of the Arizona Self-sufficiency Matrix and PRAPARE [Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences]) and continue with the longitudinal tracking of progress toward goals. A user-specific automated alerts-based dashboard displays clients needing follow-up and upcoming events. The database contains over 55,000 documented encounters across more than 5079 clients. Available resources from over 2500 community organizations have been documented. Survey data indicated that 84% (27/32) of the respondents considered the overall navigation of the database as very easy. The majority of the respondents indicated they were overall very satisfied (14/32, 44%) or satisfied (15/32, 48%) with the database. Open-ended responses indicated the database features, documentation of community organizations and visual confirmation of consent form and data storage on a Health Insurance Portability and Accountability Act-compliant record system, improved client engagement, enrollment processes, and identification of resources. CONCLUSIONS: Our database extends beyond conventional electronic medical records and provides flexibility for ever-changing needs. The COPE database provides real-world data on CHW accomplishments, thereby improving the uniformity of data collection to enhance monitoring and evaluation. This database can serve as a model for community-based documentation systems and be adapted for use in other community settings.

Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example.

BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV. OBJECTIVE: A given HIV clinic's electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure. METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware. RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate. CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic's EHR without the resource-intensive elucidation of an informative prior.

The Effect of an Electronic Medical Record-Based Clinical Decision Support System on Adherence to Clinical Protocols in Inflammatory Bowel Disease Care: Interrupted Time Series Study.

Background: Clinical decision support systems (CDSSs) embedded in electronic medical records (EMRs), also called electronic health records, have the potential to improve the adoption of clinical guidelines. The University of Alberta Inflammatory Bowel Disease (IBD) Group developed a CDSS for patients with IBD who might be experiencing disease flare and deployed it within a clinical information system in 2 continuous time periods. Objective: This study aims to evaluate the impact of the IBD CDSS on the adherence of health care providers (ie, physicians and nurses) to institutionally agreed clinical management protocols. Methods: A 2-period interrupted time series (ITS) design, comparing adherence to a clinical flare management protocol during outpatient visits before and after the CDSS implementation, was used. Each interruption was initiated with user training and a memo with instructions for use. A group of 7 physicians, 1 nurse practitioner, and 4 nurses were invited to use the CDSS. In total, 31,726 flare encounters were extracted from the clinical information system database, and 9217 of them were manually screened for inclusion. Each data point in the ITS analysis corresponded to 1 month of individual patient encounters, with a total of 18 months of data (9 before and 9 after interruption) for each period. The study was designed in accordance with the Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI) guidelines for health informatics evaluations. Results: Following manual screening, 623 flare encounters were confirmed and designated for ITS analysis. The CDSS was activated in 198 of 623 encounters, most commonly in cases where the primary visit reason was a suspected IBD flare. In Implementation Period 1, before-and-after analysis demonstrates an increase in documentation of clinical scores from 3.5% to 24.1% (P<.001), with a statistically significant level change in ITS analysis (P=.03). In Implementation Period 2, the before-and-after analysis showed further increases in the ordering of acute disease flare lab tests (47.6% to 65.8%; P<.001), including the biomarker fecal calprotectin (27.9% to 37.3%; P=.03) and stool culture testing (54.6% to 66.9%; P=.005); the latter is a test used to distinguish a flare from an infectious disease. There were no significant slope or level changes in ITS analyses in Implementation Period 2. The overall provider adoption rate was moderate at approximately 25%, with greater adoption by nurse providers (used in 30.5% of flare encounters) compared to physicians (used in 6.7% of flare encounters). Conclusions: This is one of the first studies to investigate the implementation of a CDSS for IBD, designed with a leading EMR software (Epic Systems), providing initial evidence of an improvement over routine care. Several areas for future research were identified, notably the effect of CDSSs on outcomes and how to design a CDSS with greater utility for physicians. CDSSs for IBD should also be evaluated on a larger scale; this can be facilitated by regional and national centralized EMR systems.

Development of Recommendations for the Digital Sharing of Notes With Adolescents in Mental Health Care: Delphi Study.

Background: In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice. Objective: The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals' digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations. Methods: A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus. Results: Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents. Conclusions: A total of 17 recommendations related to key aspects of health care professionals' digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice.

The Current Status of Secondary Use of Claims, Electronic Medical Records, and Electronic Health Records in Epidemiology in Japan: Narrative Literature Review.

BACKGROUND: Real-world data, such as claims, electronic medical records (EMRs), and electronic health records (EHRs), are increasingly being used in clinical epidemiology. Understanding the current status of existing approaches can help in designing high-quality epidemiological studies. OBJECTIVE: We conducted a comprehensive narrative literature review to clarify the secondary use of claims, EMRs, and EHRs in clinical epidemiology in Japan. METHODS: We searched peer-reviewed publications in PubMed from January 1, 2006, to June 30, 2021 (the date of search), which met the following 3 inclusion criteria: involvement of claims, EMRs, EHRs, or medical receipt data; mention of Japan; and published from January 1, 2006, to June 30, 2021. Eligible articles that met any of the following 6 exclusion criteria were filtered: review articles; non-disease-related articles; articles in which the Japanese population is not the sample; articles without claims, EMRs, or EHRs; full text not available; and articles without statistical analysis. Investigations of the titles, abstracts, and full texts of eligible articles were conducted automatically or manually, from which 7 categories of key information were collected. The information included organization, study design, real-world data type, database, disease, outcome, and statistical method. RESULTS: A total of 620 eligible articles were identified for this narrative literature review. The results of the 7 categories suggested that most of the studies were conducted by academic institutes (n=429); the cohort study was the primary design that longitudinally measured outcomes of proper patients (n=533); 594 studies used claims data; the use of databases was concentrated in well-known commercial and public databases; infections (n=105), cardiovascular diseases (n=100), neoplasms (n=78), and nutritional and metabolic diseases (n=75) were the most studied diseases; most studies have focused on measuring treatment patterns (n=218), physiological or clinical characteristics (n=184), and mortality (n=137); and multivariate models were commonly used (n=414). Most (375/414, 90.6%) of these multivariate modeling studies were performed for confounder adjustment. Logistic regression was the first choice for assessing many of the outcomes, with the exception of hospitalization or hospital stay and resource use or costs, for both of which linear regression was commonly used. CONCLUSIONS: This literature review provides a good understanding of the current status and trends in the use of claims, EMRs, and EHRs data in clinical epidemiology in Japan. The results demonstrated appropriate statistical methods regarding different outcomes, Japan-specific trends of disease areas, and the lack of use of artificial intelligence techniques in existing studies. In the future, a more precise comparison of relevant domestic research with worldwide research will be conducted to clarify the Japan-specific status and challenges.

Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.

Background: Knowledge graphs are a powerful tool for organizing knowledge, processing information and integrating scattered information, effectively visualizing the relationships among entities and supporting further intelligent applications. One of the critical tasks in building knowledge graphs is knowledge extraction. The existing knowledge extraction models in the Chinese medical domain usually require high-quality and large-scale manually labeled corpora for model training. In this study, we investigate rheumatoid arthritis (RA)-related Chinese electronic medical records (CEMRs) and address the automatic knowledge extraction task with a small number of annotated samples from CEMRs, from which an authoritative RA knowledge graph is constructed. Methods: After constructing the domain ontology of RA and completing manual labeling, we propose the MC-bidirectional encoder representation from transformers-bidirectional long short-term memory-conditional random field (BERT-BiLSTM-CRF) model for the named entity recognition (NER) task and the MC-BERT + feedforward neural network (FFNN) model for the entity extraction task. The pretrained language model (MC-BERT) is trained with many unlabeled medical data and fine-tuned using other medical domain datasets. We apply the established model to automatically label the remaining CEMRs, and then an RA knowledge graph is constructed based on the entities and entity relations, a preliminary assessment is conducted, and an intelligent application is presented. Results: The proposed model achieved better performance than that of other widely used models in knowledge extraction tasks, with mean F1 scores of 92.96% in entity recognition and 95.29% in relation extraction. This study preliminarily confirmed that using a pretrained medical language model could solve the problem that knowledge extraction from CEMRs requires a large number of manual annotations. An RA knowledge graph based on the above identified entities and extracted relations from 1,986 CEMRs was constructed. Experts verified the effectiveness of the constructed RA knowledge graph. Conclusions: In this paper, an RA knowledge graph based on CEMRs was established, the processes of data annotation, automatic knowledge extraction, and knowledge graph construction were described, and a preliminary assessment and an application were presented. The study demonstrated the viability of a pretrained language model combined with a deep neural network for knowledge extraction tasks from CEMRs based on a small number of manually annotated samples.

Impact of Electronic Medical Records Systems in Reporting HIV Health Data Indicators in Kenya.

Electronic Medical Records Systems (EMRs) improve the quality of patient care and reduce medical errors. Nevertheless, their role in health data indicator reporting performance is unclear. We assessed reporting completeness and timeliness of HIV indicator data to the national aggregate reporting system, District Health Information Software 2 (DHIS2) in Kenya. We compared the reporting performance of facilities with and without EMRs implementation for the year 2013 as EMRs uptake was in progress. The comparative analysis involved 104 facilities implemented with and 152 without KenyaEMR system on three HIV programmatic areas. There were no statistically significant differences in performance regarding reporting completeness and timeliness by facilities with or without EMRs (p-values > 0.05 on all the three areas). The KenyaEMR system assessed in this study, therefore, cannot be associated with the transformed performance in reporting health indicators. This was probably due to the fact that the EMRs do not report electronically to DHIS2. Additional analysis can be conducted to compare reporting performance once data exchange functionality is fully established between KenyaEMR and DHIS2 systems.

Medical Technologies Past and Present: How History Helps to Understand the Digital Era.

This article explores the relationship between medicine's history and its digital present through the lens of the physician-patient relationship. Today the rhetoric surrounding the introduction of new technologies into medicine tends to emphasize that technologies are disturbing relationships, and that the doctor-patient bond reflects a more 'human' era of medicine that should be preserved. Using historical studies of pre-modern and modern Western European medicine, this article shows that patient-physician relationships have always been shaped by material cultures. We discuss three activities - recording, examining, and treating - in the light of their historical antecedents, and suggest that the notion of 'human medicine' is ever-changing: it consists of social attributions of skills to physicians that played out very differently over the course of history.

Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers.

BACKGROUND: Electronic medical records (EMRs) offer the promise of computationally identifying sarcoidosis cases. However, the accuracy of identifying these cases in the EMR is unknown. OBJECTIVE: The aim of this study is to determine the statistical performance of using the International Classification of Diseases (ICD) diagnostic codes to identify patients with sarcoidosis in the EMR. METHODS: We used the ICD diagnostic codes to identify sarcoidosis cases by searching the EMRs of the San Francisco and Palo Alto Veterans Affairs medical centers and randomly selecting 200 patients. To improve the diagnostic accuracy of the computational algorithm in cases where histopathological data are unavailable, we developed an index of suspicion to identify cases with a high index of suspicion for sarcoidosis (confirmed and probable) based on clinical and radiographic features alone using the American Thoracic Society practice guideline. Through medical record review, we determined the positive predictive value (PPV) of diagnosing sarcoidosis by two computational methods: using ICD codes alone and using ICD codes plus the high index of suspicion. RESULTS: Among the 200 patients, 158 (79%) had a high index of suspicion for sarcoidosis. Of these 158 patients, 142 (89.9%) had documentation of nonnecrotizing granuloma, confirming biopsy-proven sarcoidosis. The PPV of using ICD codes alone was 79% (95% CI 78.6%-80.5%) for identifying sarcoidosis cases and 71% (95% CI 64.7%-77.3%) for identifying histopathologically confirmed sarcoidosis in the EMRs. The inclusion of the generated high index of suspicion to identify confirmed sarcoidosis cases increased the PPV significantly to 100% (95% CI 96.5%-100%). Histopathology documentation alone was 90% sensitive compared with high index of suspicion. CONCLUSIONS: ICD codes are reasonable classifiers for identifying sarcoidosis cases within EMRs with a PPV of 79%. Using a computational algorithm to capture index of suspicion data elements could significantly improve the case-identification accuracy.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets.

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Non-recovery of vancomycin-associated nephrotoxicity is related to worsening survival outcomes: Combined retrospective analyses of two real-world databases.

There has been growing concern in worsening survival and renal outcomes following vancomycin-associated nephrotoxicity (VAN) onset, but the factors associated with these phenomena remain unclear. To examine these factors, we performed a retrospective study combining the analysis of two real-world databases. Initially, the FDA Adverse Event Reporting System (FAERS) was used to evaluate the relationship between VAN and mortality using odds ratios (ORs) and 95% confidence intervals (CIs). Next, electronic medical records (EMRs) were examined in a more robust cohort for evaluation of the association between renal outcomes and worsening survival using Cox proportional hazards regression models. FAERS analysis revealed a significant correlation between VAN occurrence and increased mortality (OR: 1.30; 95% CI: 1.17-1.46). EMR analysis showed that non-recovery of VAN was associated with increased hospital mortality (hazard ratio [HR]: 4.05; 95% CI: 2.42-6.77) and 1-year mortality (HR: 3.03, 95% CI: 1.98-4.64). The HR for VAN recovery was lower for patients with acute kidney injury (AKI) stage ≥2 (HR: 0.09; 95% CI: 0.02-0.40). Thus, worsening survival outcomes were associated with non-recovery of VAN, whereby AKI stage ≥2 was a significant risk factor. Progression to severe VAN should be prevented for better survival outcomes.

Implementation of a Work-Related Asthma Screening Questionnaire in Clinical Settings: Multimethods Study.

BACKGROUND: A work-related asthma (WRA) screening questionnaire is currently being validated for implementation in clinical settings. To minimize barriers to integrating tools into clinical practice, a discussion of strategies for the implementation of the questionnaire has begun. OBJECTIVE: This study aimed to understand the benefits, feasibility, barriers, and limitations of implementing the Work-related Asthma Screening Questionnaire-Long version (WRASQ[L]) and asthma e-tools in clinical settings and propose dissemination and implementation strategies for the WRASQ(L). METHODS: This study was conducted in Kingston, Ontario, Canada, from September 2019 to August 2021. A workshop and 2 questionnaires were used to understand the benefits of and barriers to implementing the questionnaire in clinical settings. An expert advisory committee was established to develop the implementation and dissemination strategies. Workshops were semistructured and used thematic qualitative analysis to identify themes that provided an understanding of the benefits and limitations of and barriers to using the WRASQ(L), and e-tools in general, in clinical settings. Workshop participants included patients and health care providers, including physicians, nurses, and asthma educators, who were implementation specialists and expert electronic medical record users. A questionnaire focusing on providers' knowledge and awareness of WRA and another focusing on WRASQ(L) feedback was administered at the workshops. Advisory committee members from relevant stakeholders met 3 times to strategize implementation opportunities. RESULTS: A total of 6 themes were identified in the workshop: involving and addressing patient needs, novel data collection, knowledge translation, time considerations, functional and practical barriers, and human limitations. Questionnaire responses yielded positive feedback on the utility of the WRASQ(L) in clinical settings. All participants agreed that it is an easy way of collecting information on occupational and exposure history and could prompt a discussion between the health care provider and patient on how the workplace and exposures could affect one's asthma, increase awareness of WRA in patients and providers, and increase awareness of exposures in the workplace. Implementation and dissemination strategies were generated with input from the advisory committee. CONCLUSIONS: Stakeholders and workshop participants consider the WRASQ(L) to be a useful tool that satisfies many provider needs in their clinical settings. Once validated, dissemination strategies will include developing educational materials that include the WRASQ(L), linking the questionnaire to stakeholder websites or e-toolkits, translation into other languages, leveraging health care and research networks, conference presentations, and peer-reviewed publications. Implementation strategies will include integration into electronic medical records; designing multifaceted interventions; and targeting nontraditional settings such as workplaces, pharmacies, and research settings. The WRASQ(L) addresses many benefits of and barriers to implementation, as identified in the workshop themes. These themes will guide future implementation and dissemination strategies, noting that human limitations identified in providers and patients will need to be overcome for successful implementation.

Text-mining in electronic healthcare records can be used as efficient tool for screening and data collection in cardiovascular trials: a multicenter validation study.

OBJECTIVE: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial. STUDY DESIGN AND SETTING: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel. RESULTS: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%). CONCLUSION: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information.