How representative are electronic health records? A record linkage study using individual-level census data.

Meaningful Use and Electronic Medical Records for the Gastroenterology Practice

Administrative churn occurs when a household exits the Supplemental Nutrition Assistance Program (SNAP) and then returns to the program within 4 months. Although a number of studies have examined health care utilization patterns related to Medicaid administrative churn less is known about health care utilization patterns among Medicaid-insured SNAP enrollees. To investigate the characteristics and health care utilization patterns of Medicaid insured SNAP participants who experience SNAP administrative churn. Retrospective cohort study using 2010-2013 SNAP benefit data from the state of Missouri linked to Medicaid claims data for the same time period. Individual fixed effect regression analysis was used to investigate differences in health care claims for churners and nonchurners across various health care settings. Missouri residents ages 18-64 who were Medicaid-insured SNAP enrollees. Inpatient, outpatient, emergency department (ED), and pharmacy claims, and churn status. Half of our sample (49.63%) experienced administrative churn. In the descriptive analyses, churners had fewer claims for prescription drugs than nonchurners (25.42% vs. 30.47%), but more claims for ED visits (3.79% vs. 2.74%). Adjusting for individual fixed characteristics, inpatient claims occurred with more frequency during periods of churn than while on SNAP, whereas ED, outpatient, and pharmacy claims occurred with less frequency during periods of churn than while on SNAP. SNAP administrative churn was very common among our study sample. Given that health care utilization patterns varied for churners compared with nonchurners, it is important that researchers and public health professionals not assume stable SNAP receipt among participants.

This paper presents the history of the use of the computer for maintaining patient medical care information. An electronic record generated with a computer, which is non-specific for any healthcare specialty, is referred to as the electronic health record. The electronic health record was previously called the computer-based patient record. "Electronic" replaced the earlier term "computer-based" because "electronic" better describes the medium in which the patient record is managed. The electronic health record and its application to dentistry are discussed. The electronic health record is a "database" of patient information that has been entered by any healthcare provider; the electronic oral health record is an "electronic record" of oral health information that has been entered by an oral healthcare provider. The significant differences between the electronic health record and the electronic oral health record are outlined and highlighted. Included is a template describing a procedure to be used by dental personnel during the decision making process of purchasing an electronic oral health record. A brief description of a practice template is also provided. These completed templates can be shared with dental software vendors to clarify their understanding of and to clearly describe the needs of today's dental practice. The challenge of introducing information technology into educational institutions' curricula is identified. Finally, the potential benefit of using electronic technology for managing oral healthcare information is outlined.

The Progression of Electronic Health Records and Implications for Psychiatry

ObjectiveTo develop national Stage 2 Meaningful Use (MUse) recommendations for syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record (EHR) data.IntroductionMUse will make EHR data increasingly available for public health surveillance. For Stage 2, the Centers for Medicare & Medicaid Services (CMS) regulations will require hospitals and offer an option for eligible professionals to provide electronic syndromic surveillance data to public health. Together, these data can strengthen public health surveillance capabilities and population health outcomes (Figure 1).To facilitate the adoption and effective use of these data to advance population health, public health priorities and system capabilities must shape standards for data exchange. Input from all stakeholders is critical to ensure the feasibility, practicality, and, hence, adoption of any recommendations and data use guidelines.MethodsISDS, in collaboration with the Division of Informatics Solutions and Operations at the Centers for Disease Control and Prevention (CDC), and HLN Consulting, convened a multi-stakeholder Work-group of clinicians, technologists, epidemiologists, and public health officials with expertise in syndromic surveillance. Recommended MUse guidelines were developed by performing an environmental scan of current practice and by using an iterative, expert and community input-driven process. The Workgroup developed initial guidelines and then solicited and received feedback from the stakeholder community via interview, e-mail, and structured surveys. Stakeholder feedback was analyzed using quantitative and qualitative methods and used to revise the recommendations.ResultsThe MUse Workgroup defined electronic syndromic surveillance (ESS) characteristics. Specifically, data are characterized by their timeliness, sensitivity rather than specificity, population focus, limited personally identifiable information, and inclusion of all patient encounters within a specific healthcare setting (e.g., emergency department, inpatient, outpatient). Based on stakeholder input (n=125) and Workgroup expertise, the guidelines identify priority syndromic surveillance uses that can assist with:Monitoring population health;Informing public health services; andInforming interventions, health education, and policy by characterizing the burden of chronic disease and health disparities.Similarly, the Workgroup identified data elements to support these uses in the hospital inpatient setting and possibly in the ambulatory care setting. They were aligned to previously identified emergency department and urgent care center data elements and Stage 1–2 clinical MUse objectives. Core data elements (required for certification) cover treating facility; patient demographics; subjective and objective clinical findings, including chief complaint, body mass index, smoking history, diagnoses; and outcomes. Other data elements were designated as extended (not required for certification) or future (for future consideration). The data elements and their specifications are subject to change based on applicable state and local laws and practices.Based on their findings and recommended guidelines detailed in the report, the Workgroup also identified community activities and additional investments that would best support public health agencies in using EHR technology with syndromic surveillance methodologies.ConclusionsThe widespread adoption of EHRs, catalyzed by MUse, has the potential to improve population health. By identifying and describing potential ESS uses of new sources of EHR data and associated data elements with the greatest utility for public health, the recommendations set forth by the ISDS MUse Workgroup will serve to facilitate the adoption of MUse policy by both healthcare and public health agencies.

An important goal of medical education is to teach students to use an electronic health record (EHR) safely and effectively. The purpose of this study is to examine medical student accounts of EHR use during their core inpatient clinical clerkships using a national sample. Paper health records (PHRs) are similarly examined. An online survey about health record use within the inpatient component of six core clerkships was administered to medical students after they completed Step 2 Clinical Knowledge of the United States Medical Licensing Examination. The sample included 17,202 U.S. medical students graduating between 2012 and 2016. Mean percentages of clerkships in which students engaged in various health record activities were computed, and analysis of variance was used to examine differences. The mean percentages of clerkships in which a student accessed or entered information into an EHR increased from 78% to 93% and 59% to 72%, respectively. For students who used an EHR, the mean percentage of clerkships in which they entered information remained constant at 76%. Students entered notes during the majority of their clerkships, with increases over time. However, students entered orders in less than a quarter of their clerkships, with decreases over time. The percentage of clerkships in which students used PHRs was lower and declining. Although students used an EHR in the majority of their inpatient core clerkships, they received limited educational experiences related to order and note writing, which could translate into a lack of preparedness for future training and practice.

Background Frequent Attenders with Medically Unexplained Symptoms (FA/MUS) are common in primary care, though challenging to identify and treat. Objectives This study sought to compare FA/MUS to FA with organic illnesses (FA/OI) and the general clinic population (Non-FA) to understand their demographic characteristics and healthcare utilisation patterns. Methods For this retrospective, observational study, Electronic Medical Records (EMR) were obtained from Clalit Health Services, regarding the population of a sizeable primary care clinic in Be’er-Sheva, Israel. Electronic medical records were screened to identify the top 5% of FA. FA were stratified based on whether they had OI. FA without OI were then corroborated as having MUS by their physicians. Demographics, healthcare utilisation and costs were analysed for FA/OI, FA/MUS and Non-FA. Results Out of 594 FA, 305 (53.6%) were FA/OI and 264 (46.4%) were FA/MUS. FA/OI were older (69.1 vs. 56.4 years, p<.001) and costlier (ILS27693 vs. ILS9075, p<.001) than FA/MUS. Average costs for FA/MUS were over four times higher than Non-FA (ILS9075 vs. ILS2035, p<.001). The largest disparities between FA/OI and FA/MUS were in hospitalisations (ILS6998 vs. ILS2033) and surgical procedures (ILS8143 vs. ILS3175). Regarding laboratory tests, differences were smaller between groups of FA but significantly different between FA and Non-FA. Conclusion FA/MUS are more costly than Non-FA and exhibit unique healthcare utilisation and costs patterns. FA/OI had more severe illnesses necessitating hospitalisations and surgical interventions, while FA/MUS had more investigations and tests, attempting to find an explanation for their symptoms.

Is Electronic Health Record Safety a Paradox?

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data

e-Liability and e-Documentation.

Towards Estimating Childhood Obesity Prevalence Using Electronic Health Records

Health-related socioeconomic risk screening in outpatient obstetrics and gynecology practice

Older adults receiving food assistance may have varying health care needs and resource levels; however very little is known about their health care utilization patterns. This study examined the relationship of food assistance program participation with health care utilization in homebound older Medicare beneficiaries in Georgia in need of Supplemental Nutrition Assistance Program (SNAP) and home delivered meals program (HDM) using the self‐administered survey and Medicare claims data (n=1,270, mean age 79.4, 69.9% female, 34.4 %black). Health care utilization during 2009 (i.e., inpatient, outpatient, physician, and home health visits) was compared across four levels of food assistance program participation (i.e., both SNAP and HDM, SNAP only, HDM only, and nonparticipation) in the study sample using negative binomial regression while controlling for potential confounders. About 22% received both SNAP and HDM, and 24.4% and 17.6% received either SNAP or HDM, respectively. Those receiving any food assistance were more likely to be younger, black, less educated, Medicare dual eligible, living in rural areas and alone, and food insecure. Those receiving both programs showed poorest socioeconomic and health characteristics and used higher outpatient and physician services than nonparticipants. These findings suggest higher burden of both healthcare and food insecurity experienced by vulnerable homebound older adults.

Autoverification (AV) is the process in which a medication is automatically verified in the electronic health record (EHR), bypassing a pharmacist's approval. If concerns of safety and efficacy for AV are addressed, broad implementation can allow AV to be a powerful tool within a hospital system to verify high-volume, low-risk medication orders. This study aims to identify parameters for risk stratification of medications and develop a replicable framework model for identifying medications appropriate for AV at UNC Health. The modified Delphi methodology was utilized to reach consensus on parameters used in a risk stratification tool for medication orders. This tool was applied retroactively to a sample of medication orders at UNC Health during a 1-month period (October 2023) to determine risk of adverse event for potentially autoverified orders. Fifty-five criteria met consensus for consideration for use for an AV risk appraisal tool. Results from a consensus meeting for criteria that would be used in the autoverification risk appraisal tool (AVRAT) to flag medication orders as "high-risk for AV" were age, estimated glomerular filtration rate, hemoglobin level, platelet count, body weight, and EHR documentation of continuous renal replacement therapy. Twenty medications were selected for an initial proof-of-concept evaluation of the AVRAT. Using AVRAT criteria, it was determined that a total of 6.89% of all October medication orders at UNC Health posed a low risk of a potential adverse event with AV. A proof-of-concept study for the utilization of AV was effectively developed. The study results indicated that AV can possibly reduce time for medication order review across a hospital system, with a relatively small number of orders being potentially eligible for AV.

Meaningful Use: How Did We Do, Where Are We Now, Where Do We Go from Here?