Personalized Medicine and the Power of Electronic Health Records.

Personalized medicine has largely been enabled by the integration of genomic and other data with electronic health records (EHRs) in the United States and elsewhere. Increased EHR adoption across various clinical settings and the establishment of EHR-linked population-based biobanks provide unprecedented opportunities for the types of translational and implementation research that drive personalized medicine. We review advances in the digitization of health information and the proliferation of genomic research in health systems and provide insights into emerging paths for the widespread implementation of personalized medicine.

Challenges of EHR "Meaningful Use" in Rural Health Centers.

The Centers for Disease Control and Prevention (CDC) continues to promote the utilization of electronic health records (EHRs) to support population health management and reduce disparities. However, access to EHRs with capabilities to disaggregate data or generate digital dashboards is not always readily available in rural areas. With funding from CDC's DP-18-1815, the Division of Diabetes and Heart Disease Management (Division) at the South Carolina Department of Health and Environmental Control designed a quality improvement initiative to reduce health disparities for people with hypertension and high blood cholesterol in rural areas. With support from a nonprofit partner, the Division used qualitative evaluation methods to evaluate the extent to which practices were able to disaggregate data and report quality measures.

[Digital health progress hubs-data integration beyond university hospitals]. / Die digitalen Fortschrittshubs Gesundheit ­ Gemeinsame Datennutzung über die Universitätsmedizin hinaus.

The digital health progress hubs pilot the extensibility of the concepts and solutions of the Medical Informatics Initiative to improve regional healthcare and research. The six funded projects address different diseases, areas in regional healthcare, and methods of cross-institutional data linking and use. Despite the diversity of the scenarios and regional conditions, the technical, regulatory, and organizational challenges and barriers that the progress hubs encounter in the actual implementation of the solutions are often similar. This results in some common approaches to solutions, but also in political demands that go beyond the Health Data Utilization Act, which is considered a welcome improvement by the progress hubs.In this article, we present the digital progress hubs and discuss achievements, challenges, and approaches to solutions that enable the shared use of data from university hospitals and non-academic institutions in the healthcare system and can make a sustainable contribution to improving medical care and research.

LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.

Percentage of Gleason pattern 4 and tumor volume predict adverse pathological stage and margin status at radical prostatectomy in grade Group 2 and grade Group 3 prostate cancers.

BACKGROUND: Increasing percentages of Gleason pattern 4 (GP4%) in radical prostatectomy (RP) correlate with an increased likelihood of nonorgan-confined disease and earlier biochemical recurrence (BCR). However, there are no detailed RP studies assessing the impact of GP4% and corresponding tumor volume (TV) on extraprostatic extension (EPE), seminal vesicle (SV) invasion (SV+), and positive surgical margin (SM) status (SM+). METHODS: In 1301 consecutive RPs, we analyzed each tumor nodule (TN) for TV, Grade Group (GG), presence of focal versus nonfocal EPE, SV+ , and SM+. Using GG1 (GP4% = 0) TNs as a reference, we recorded GP4% for all GG2 or GG3 TNs. We performed a multivariable analysis (MVA) using a mixed effects logistic regression that tested significant variables for risk of EPE, SV+, and SM+, as well as a multinomial logistic regression model that tested significant variables for risks of nonorgan-confined disease (pT2+, pT3a, and pT3b) versus organ-confined disease (pT2). RESULTS: We identified 3231 discrete TNs ranging from 1 to 7 (median: 2.5) per RP. These included GG1 (n = 2115), GG2 (n = 818), GG3 (n = 274), and GG4 (n = 24) TNs. Increasing GP4% weakly paralleled increasing TV (tau = 0.07, p < .001). In MVA, increasing GP4% and TV predicted a greater likelihood of EPE (odds ratio [OR]: 1.03 and 4.41), SV+ (OR: 1.03 and 3.83), and SM+ (1.01, p = .01 and 2.83), all p < .001. Our multinomial logistic regression model demonstrated an association between GP4% and the risk of EPE (i.e., pT3a and pT3b disease), as well as an association between TV and risk of upstaging (all p < .001). CONCLUSIONS: Both GP4% and TV are independent predictors of adverse pathological stage and margin status at RP. However, the risks for adverse outcomes associated with GP4% are marginal, while those for TV are strong. The prognostic significance of GP4% on BCR-free survival has not been studied controlling for TV and other adverse RP findings. Whether adverse pathological stage and margin status associated with larger TV could decrease BCR-free survival to a greater extent than increasing RP GP4% remains to be studied.

A Trend-Based Early Warning Score Can Be Implemented in a Hospital Electronic Medical Record to Effectively Predict Inpatient Deterioration.

OBJECTIVES: To determine whether a statistically derived, trend-based, deterioration index is superior to other early warning scores at predicting adverse events and whether it can be integrated into an electronic medical record to enable real-time alerts. DESIGN: Forty-three variables and their trends from cases and controls were used to develop a logistic model and deterioration index to predict patient deterioration greater than or equal to 1 hour prior to an adverse event. SETTING: Two large Australian teaching hospitals. PATIENTS: Cases were considered as patients who suffered adverse events (unexpected death, unplanned ICU transfer, urgent surgery, and rapid-response alert) between August 1, 2016, and April 1, 2019. INTERVENTIONS: The logistic model and deterioration index were tested on historical data and then integrated into an electronic medical record for a 6-month prospective "silent" validation. MEASUREMENTS AND MAIN RESULTS: Data were acquired from 258,732 admissions. There were 8,002 adverse events. The addition of vital sign and laboratory trend values to the logistic model increased the area under the curve from 0.84 to 0.89 and the sensitivity to predict an adverse event 1-48 hours prior from 0.35 to 0.41. A 48-hour simulation showed that the logistic model had a higher area under the curve than the Modified Early Warning Score and National Early Warning Score (0.87 vs 0.74 vs 0.71). During the silently run prospective trial, the sensitivity of the deterioration index to detect adverse event any time prior to the adverse event was 0.474, 0.369 1 hour prior, and 0.327 4 hours prior, with a specificity of 0.972. CONCLUSIONS: A deterioration prediction model was developed using patient demographics, ward-based observations, laboratory values, and their trends. The model's outputs were converted to a deterioration index that was successfully integrated into a live hospital electronic medical record. The sensitivity and specificity of the tool to detect inpatient deterioration were superior to traditional early warning scores.

Can Big Data guide prognosis and clinical decisions in epilepsy?

Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

Precision Medicine: Academic dreaming or clinical reality?

Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient-level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is useful, but not readily applicable to an individual patient. This has brought into sharp focus the desire for a more individualized approach through which we counsel people based on individual characteristics, as opposed to population-level data. We are now accruing data at unprecedented rates, allowing us to convert this ideal into reality. In addition, we have access to growing volumes of administrative and electronic health records data, biometric, imaging, genetics data, microbiome, and other "omics" data, thus paving the way toward phenome-wide association studies and "the epidemiology of one." Despite this, there are many challenges ahead. The collating, integrating, and storing sensitive multimodal data for advanced analytics remains difficult as patient consent and data security issues increase in complexity. Agreement on many aspects of epilepsy remains imperfect, rendering models sensitive to misclassification due to a lack of "ground truth." Even with existing data, advanced analytics models are prone to overfitting and often failure to generalize externally. Finally, uptake by clinicians is often hindered by opaque, "black box" algorithms. Systematic approaches to data collection and model generation, and an emphasis on education to promote uptake and knowledge translation, are required to propel epilepsy-based precision medicine from the realm of the theoretical into routine clinical practice.

Ambulatory care for epilepsy via telemedicine during the COVID-19 pandemic.

OBJECTIVE: To assess feasibility, patient satisfaction, and financial advantages of telemedicine for epilepsy ambulatory care during the current COVID-19 pandemic. METHODS: The demographic and clinical characteristics of all consecutive patients evaluated via telemedicine at a level 4 epilepsy center between March 20 and April 20, 2020 were obtained retrospectively from electronic medical records. A telephone survey to assess patient satisfaction and preferences was conducted within one month following the initial visit. RESULTS: Among 223 telehealth patients, 85.7% used both synchronous audio and video technology. During the visits, 39% of patients had their anticonvulsants adjusted while 18.8% and 11.2% were referred to laboratory/diagnostic testing and specialty consults, respectively. In a post-visit survey, the highest degree of satisfaction with care was expressed by 76.9% of patients. The degree of satisfaction tended to increase the further a patient lived from the clinic (p = 0.05). Beyond the pandemic, 89% of patients reported a preference for continuing telemedicine if their epilepsy symptoms remained stable, while only 44.4% chose telemedicine should their symptoms worsen. Inclement weather and lack of transportation were factors favoring continued use of telemedicine. An estimated cost saving to patient attributed to telemedicine was $30.20 ±â€¯3.8 per visit. SIGNIFICANCE: Our findings suggest that epilepsy care via telemedicine provided high satisfaction and economic benefit, without compromising patients' quality of care, thereby supporting the use of virtual care during current and future epidemiological fallouts. Beyond the current pandemic, patients with stable seizure symptoms may prefer to use telemedicine for their epilepsy care.

Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.

In line with the current trajectory of healthcare reform, significant emphasis has been placed on improving the utilization of data collected during a clinical encounter. Although the structured fields of electronic health records have provided a convenient foundation on which to begin such efforts, it was well understood that a substantial portion of relevant information is confined in the free-text narratives documenting care. Unfortunately, extracting meaningful information from such narratives is a non-trivial task, traditionally requiring significant manual effort. Today, computational approaches from a field known as Natural Language Processing (NLP) are poised to make a transformational impact in the analysis and utilization of these documents across healthcare practice and research, particularly in procedure-heavy sub-disciplines such as gastroenterology (GI). As such, this manuscript provides a clinically focused review of NLP systems in GI practice. It begins with a detailed synopsis around the state of NLP techniques, presenting state-of-the-art methods and typical use cases in both clinical settings and across other domains. Next, it will present a robust literature review around current applications of NLP within four prominent areas of gastroenterology including endoscopy, inflammatory bowel disease, pancreaticobiliary, and liver diseases. Finally, it concludes with a discussion of open problems and future opportunities of this technology in the field of gastroenterology and health care as a whole.

Patients' Perspectives About Factors Affecting Their Use of Electronic Personal Health Records in England: Qualitative Analysis.

BACKGROUND: General practices (GPs) in England have recently introduced a nationwide electronic personal health record (ePHR) system called Patient Online or GP online services, which allows patients to view parts of their medical records, book appointments, and request prescription refills. Although this system is free of charge, its adoption rates are low. To improve patients' adoption and implementation success of the system, it is important to understand the factors affecting their use of the system. OBJECTIVE: The aim of this study is to explore patients' perspectives of factors affecting their use of ePHRs in England. METHODS: A cross-sectional survey was carried out between August 21 and September 26, 2017. A questionnaire was used in this survey to collect mainly quantitative data through closed-ended questions in addition to qualitative data through an open-ended question. A convenience sample was recruited in 4 GPs in West Yorkshire, England. Given that the quantitative data were analyzed in a previous study, we analyzed the qualitative data using thematic analysis. RESULTS: Of the 800 eligible patients invited to participate in the survey, 624 (78.0%) returned a fully completed questionnaire. Of those returned questionnaires, the open-ended question was answered by 136/624 (21.8%) participants. A total of 2 meta-themes emerged from participants' responses. The first meta-theme comprises 5 themes about why patients do not use Patient Online: concerns about using Patient Online, lack of awareness of Patient Online, challenges regarding internet and computers, perceived characteristics of nonusers, and preference for personal contact. The second meta-theme contains 1 theme about why patients use Patient Online: encouraging features of Patient Online. CONCLUSIONS: The challenges and concerns that impede the use of Patient Online seem to be of greater importance than the facilitators that encourage its use. There are practical considerations that, if incorporated into the system, are likely to improve its adoption rate: Patient Online should be useful, easy to use, secure, and easy to access. Different channels should be used to increase the awareness of the system, and GPs should ease registration with the system and provide manuals, training sessions, and technical support. More research is needed to assess the effect of the new factors found in this study (eg, lack of trust, difficulty registering with Patient Online) and factors affecting the continuing use of the system.

Four Years Later: Examining Nurse Perceptions of Electronic Documentation Over Time.

OBJECTIVE: To examine changes in registered nurse (RN) perceptions of electronic documentation over a 4-year period. BACKGROUND: The investigators previously reported differences in RN perceptions prior to and 1 year after adoption of a comprehensive electronic health record (EHR). METHODS: Investigators repeated the study 4 years after adoption, using the Nurses' Perceptions of Electronic Documentation tool and interviews with a subset of RNs. RESULTS: Nurses scored higher on ease of use domain and lower on concern about the EHR domain and showed no difference on the impacts of the EHR domain. Interviews revealed that 4 years later, some aspects of documentation were easier; the tool was comprehensive, but not without risk, and nurses remained ambivalent about the EHR. CONCLUSIONS: Use of EHR technology impacts nursing work. It is important to understand how nurses' perceptions change over time. This study gives nursing leaders insight into adoption and acceptance of an EHR.

Genomics-First Evaluation of Heart Disease Associated With Titin-Truncating Variants.

BACKGROUND: Truncating variants in the Titin gene (TTNtvs) are common in individuals with idiopathic dilated cardiomyopathy (DCM). However, a comprehensive genomics-first evaluation of the impact of TTNtvs in different clinical contexts, and the evaluation of modifiers such as genetic ancestry, has not been performed. METHODS: We reviewed whole exome sequence data for >71 000 individuals (61 040 from the Geisinger MyCode Community Health Initiative (2007 to present) and 10 273 from the PennMedicine BioBank (2013 to present) to identify anyone with TTNtvs. We further selected individuals with TTNtvs in exons highly expressed in the heart (proportion spliced in [PSI] >0.9). Using linked electronic health records, we evaluated associations of TTNtvs with diagnoses and quantitative echocardiographic measures, including subanalyses for individuals with and without DCM diagnoses. We also reviewed data from the Jackson Heart Study to validate specific analyses for individuals of African ancestry. RESULTS: Identified with a TTNtv in a highly expressed exon (hiPSI) were 1.2% individuals in PennMedicine BioBank and 0.6% at Geisinger. The presence of a hiPSI TTNtv was associated with increased odds of DCM in individuals of European ancestry (odds ratio [95% CI]: 18.7 [9.1-39.4] {PennMedicine BioBank} and 10.8 [7.0-16.0] {Geisinger}). hiPSI TTNtvs were not associated with DCM in individuals of African ancestry, despite a high DCM prevalence (odds ratio, 1.8 [0.2-13.7]; P=0.57). Among 244 individuals of European ancestry with DCM in PennMedicine BioBank, hiPSI TTNtv carriers had lower left ventricular ejection fraction (ß=-12%, P=3×10-7), and increased left ventricular diameter (ß=0.65 cm, P=9×10-3). In the Geisinger cohort, hiPSI TTNtv carriers without a cardiomyopathy diagnosis had more atrial fibrillation (odds ratio, 2.4 [1.6-3.6]) and heart failure (odds ratio, 3.8 [2.4-6.0]), and lower left ventricular ejection fraction (ß=-3.4%, P=1×10-7). CONCLUSIONS: Individuals of European ancestry with hiPSI TTNtv have an abnormal cardiac phenotype characterized by lower left ventricular ejection fraction, irrespective of the clinical manifestation of cardiomyopathy. Associations with arrhythmias, including atrial fibrillation, were observed even when controlling for cardiomyopathy diagnosis. In contrast, no association between hiPSI TTNtvs and DCM was discerned among individuals of African ancestry. Given these findings, clinical identification of hiPSI TTNtv carriers may alter clinical management strategies.

Genomics and electronic health record systems.

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring.

Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.

Impact of pre-exposure time bias in self-controlled case series when the event conditions the exposure: Hip/femur fracture and use of benzodiazepines as a case study.

BACKGROUND: In self-controlled case series (SCCS), the event should not condition the probability of subsequent exposure. If this assumption is not met, an important bias could take place. The association of hip/femur fracture (HFF) and use of benzodiazepines (BDZ) has a bidirectional causal relationship and can serve as case study to investigate the impact of this methodological issue. OBJECTIVES: To assess the magnitude of bias introduced in a SCCS when HFF conditions the posterior exposure to BDZ and explore ways to correct it. METHODS: Four thousand four hundred fifty cases of HFF who had at least one BZD prescription were selected from the primary care health record database BIFAP. Exposure to BZD was divided into non-use, current, recent, and past use. Conditional Poisson regression was used to estimate incidence rate ratios (IRRs) of HFF among current vs non-use/past, adjusted for age. To investigate possible event-exposure dependence, a pre-exposure time of different lengths (15, 30, and 60 days) was excluded from the reference category to evaluate the IRR. RESULTS: IRR of HHF for current use was 0.79 (0.72-0.86); removing 30 days, IRR was 1.43 (1.31-1.57). Removing 15 days, IRR was 1.29 (1.18-1.41), and removing 60 days, IRR was 1.56 (1.42-1.72). A pre-exposure period up to 182 days was necessary to remove such effect giving an IRR of 1.64 (1.48-1.81). CONCLUSIONS: HFF remarkably conditioned the use of BDZs resulting in seriously biased IRRs when this association was studied through a SCCS design. The use of pre-exposure periods of different lengths helped to correct this error.

Derivation, Validation, Sustained Performance, and Clinical Impact of an Electronic Medical Record-Based Perioperative Delirium Risk Stratification Tool.

BACKGROUND: Postoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care. METHODS: The risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium. RESULTS: The logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends. CONCLUSIONS: The AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.