Remote Monitoring of Patient- and Family-Generated Health Data in Pediatrics.

In this article, we provide an overview of remote monitoring of pediatric PGHD and family-generated health data, including its current uses, future opportunities, and implementation resources.

Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies.

Electronic health record (EHR) discontinuity (i.e., receiving care outside of the study EHR system), can lead to information bias in EHR-based real-world evidence (RWE) studies. An algorithm has been previously developed to identify patients with high EHR-continuity. We sought to assess whether applying this algorithm to patient selection for inclusion can reduce bias caused by data-discontinuity in four RWE examples. Among Medicare beneficiaries aged >=65 years from 2007 to 2014, we established 4 cohorts assessing drug effects on short-term or long-term outcomes, respectively. We linked claims data with two US EHR systems and calculated %bias of the multivariable-adjusted effect estimates based on only EHR vs. linked EHR-claims data because the linked data capture medical information recorded outside of the study EHR. Our study cohort included 77,288 patients in system 1 and 60,309 in system 2. We found the subcohort in the lowest quartile of EHR-continuity captured 72-81% of the short-term and only 21-31% of the long-term outcome events, leading to %bias of 6-99% for the short-term and 62-112% for the long-term outcome examples. This trend appeared to be more pronounced in the example using a nonuser comparison rather than an active comparison. We did not find significant treatment effect heterogeneity by EHR-continuity for most subgroups across empirical examples. In EHR-based RWE studies, investigators may consider excluding patients with low algorithm-predicted EHR-continuity as the EHR data capture relatively few of their actual outcomes, and treatment effect estimates in these patients may be unreliable.

Registro Nacional de Quemados Chile: presentación de una plataforma online y mirada al pronóstico de pacientes no trasladados a Centro de Quemados / Presentation of an online National Registration Platform for severely burned patients in the Chilean Public Health System and a look into the prognosis of patients not transferred to the National Reference Burn Center

Resumen Introducción: En el año 2017 se incorporó un registro de notificación en línea (Registro Nacional de Quemados) al flujo de derivación de pacientes quemados en Chile. Objetivo: A partir de la información obtenida de esta plataforma, se describe la epidemiología de las quemaduras y las variables que podrían explicar los traslados fallidos a nuestra unidad de quemados. Materiales y Método: Se analizaron los casos subidos a esta plataforma entre julio de 2017 y julio de 2018. Se caracterizó la población global y comparó variables relevantes entre el grupo de pacientes no trasladados a nuestra unidad y los que fueron trasladados con éxito. Resultados: Se analizaron 319 pacientes, 66% hombres, edad promedio 51 años, IMC de 27% y 47% con enfermedades previas. El fuego fue la principal causa de quemaduras. Se observó un 31% de injuria inhaladora. 107 pacientes no se trasladaron a nuestro centro de quemados. Los pacientes trasladados puntuaron más alto en comorbilidad, índice de gravedad, superficie corporal total quemada y aseo quirúrgico en el hospital base. El grupo de pacientes no trasladados puntuó más alto en injuria inhalatoria. La mortalidad global fue 20,4%. La mortalidad fue mayor en pacientes no trasladados (33,6% versus 13,7%; p < 0,001). Conclusiones: Además de facilitar el flujo de pacientes y ahorrar recursos, un uso noble de esta plataforma es ser fuente de información epidemiológica y de implementación de políticas públicas, lo cual puede ser tomado como ejemplo por otros países en vías de desarrollo. Además, se demuestra que ser trasladado constituye un factor protector de muerte por quemaduras.

Introduction: In 2017, an online notification register, the National Burn Registry, was incorporated into the referral flow of burned patients in Chile. Aim: Through the information obtained from this platform, we describe the epidemiology of burns in Chile, and identify variables that could explain failed transfers to our burn unit. Materials and Method: Cases uploaded to this platform between July 2017 - July 2018 were analyzed. We characterize the global population and relevant variables were compared between the group of patients that failed to be transferred to the burn unit and the ones who were successfully transferred. Results: 319 patients were analyzed, 66% men, average age 51 years, BMI of 27 and 47% with previous illnesses. Fire was the main cause of burn injury. Smoke inhalation injury was observed for 31%. 107 patients failed to reach to our burn center. Transferred patients rated higher in comorbidity, severity index, total burned body surface and surgical debridement at base hospital. The group of not transferred patients rated higher in inhalation injury. Overall mortality was 20.4%. Mortality was higher in non-transferred patients (33.6% versus 13.7%; p < 0.001). Conclusions: Aside from facilitating the flow of burned patients and resources saving, a noble use of this platform has been to serve as a source of epidemiological information and implementation of public policies, which can be taken as an example by other developing countries. Also, being transferred is a protective factor for death from burn injuries.

Utilization, Determinants, and Prospects of Electronic Medical Records in Ethiopia.

BACKGROUND: A lot of effort is being done in the electronic medical record (EMR) system. However, it has not been implemented and used at the expected scale for maximal effectiveness. There is limited evidence on the factors affecting the utilization of EMR in this particular context, which are critical for targeted strategies. OBJECTIVE: To assess the magnitude and factors affecting the utilization of EMR among health professionals in eastern Ethiopia. METHODS: An institutional-based cross-sectional study was conducted among randomly selected 412 health professionals from Harari and Dire Dawa, eastern Ethiopia, using a pretested self-administered questionnaire. The tool was developed from previous literature, and a pilot survey was done before the actual study. Bivariable and multivariable binary logistic regression were done to assess the relationship between an independent variable with EMR use. Crude and an adjusted odds ratio with a 95% confidence interval were reported. A P value of less than 0.05 was used to declare a statistically significant association. RESULTS: A total of 412 health professionals with a mean age of 29 years (±6.4 years) were included. A total of 229 (55.6%) and 300 (72.8%) of them had good knowledge and attitude towards the EMR, while 279 (67.7%) used the service (54% used it on a daily basis). About 272 (66%) of the respondents reported that they prefer EMRs to paper-based systems. Health professionals with more than five years of experience had two times higher odds of using the service (AOR = 2.22; 95% CI; 1.12-4.42) than early-career workers. Health professionals trained in EMR would use the service more (AOR = 5.88; 95% CI; 2.93-11.88) compared to those who did not take the training. In addition, having good knowledge (AOR = 1.52; 95% CI; 0.92-1.5) and a good attitude towards the EMR system (AOR = 2.4; 95% CI; 1.35-4.31) showed to use EMR as compared to counterparts. CONCLUSIONS: The utilization of EMR was found to be optimal. Age, work experience, knowledge, attitude, and training of professionals were positively associated with the use of the service in their facility.

Influential Usage of Big Data and Artificial Intelligence in Healthcare.

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

Trends Associated with Hemorrhoids in Japan: Data Mining of Medical Information Datasets and the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open Data Japan.

Hemorrhoids are a common anorectal disease. Epidemiological studies on medication trends and risk factors using information from real-world databases are rare. Our objective was to analyze the relationship between hemorrhoid treatment prescription trends and several risk factors using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open Data Japan and related medical information datasets. We calculated the standardized prescription ratio (SPR) based on the 2nd NDB Open Data Japan from 2015. The correlation coefficients between the SPR of antihemorrhoidals and those of "antispasmodics," "antiarrhythmic agents," "antidiarrheals, intestinal regulators," "purgatives and clysters," "hypnotics and sedatives, antianxietics," "psychotropic agents," and "opium alkaloids preparations" were 0.7474, 0.7366, 0.7184, 0.6501, 0.6320, 0.4571, and 0.4542, respectively. The correlation coefficient between the SPR of antihemorrhoidals and those of "average annual temperature," "percentage of people who were smokers," and "percentage of people who drank regularly" were -0.7204, 0.6002, and 0.3537, respectively. The results of cluster analysis revealed that Hokkaido and Tohoku regions tended to have low average annual temperature values and high percentage of people who were smokers and had comparatively high SPRs of "antispasmodics," "antiarrhythmic agents," "antidiarrheals, intestinal regulators," "purgatives and clysters," "hypnotics and sedatives, antianxietics," "psychotropic agents," and "opium alkaloids preparations." Antihemorrhoidals are frequently used in Hokkaido and Tohoku, Japan; thus, it is important for these prefectural governments to focus on these factors when taking measures regarding health promotion.

Predicting Incident Treatment-Resistant Depression: A Model Designed for Health Systems of Care.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and debilitating condition. While numerous treatment options are available, low treatment response and high remission rates remain common, leading to the concept of treatment-resistant depression (TRD): a classification applied to patients who fail multiple courses of therapy. A patient with TRD can only be identified after repeated, and often prolonged, therapeutic efforts. OBJECTIVE: To use data readily available to integrated delivery networks to identify characteristics predictive of TRD among patients initiating pharmacotherapy for MDD. METHODS: Decision Resources Group Real-World Data, an integrated medical/pharmacy claims and electronic health record dataset, was used to conduct a retrospective, longitudinal cohort study of patients with MDD who initiated antidepressant treatment between July 1, 2014, and December 31, 2015. Individuals were followed for 24 months to determine treatment resistance. Eligible individuals had integrated claims and electronic health record data available, completed at least 1 course of therapy of adequate dose and duration to achieve response, and had 30 months of continuous benefits eligibility (6 months before and 24 months after treatment initiation). Stepwise logistic regression and demographic, health history, health care utilization, medication, provider, and related characteristics were used to predict onset of TRD. RESULTS: 35,246 people met eligibility and 7,098 (20.1%) met TRD criteria after an average of 402 days. Significant predictors of TRD included patient age, diagnosis of insomnia and hypertension, psychiatric office visits, nurse telephonic encounters, anticonvulsant medication use, suicidality, physician specialty associated with index prescription, total prescription drug claims, unique antidepressants attempted, and duration of untreated illness (the lag between diagnosis and index prescription). The final model achieved an area under the curve (AUC) = 0.83. Structured patient-generated health data, specifically, the Patient Health Questionnaire-2 and the Patient Health Questionnaire-9 were only reported for 542 patients (1.5%). CONCLUSIONS: TRD transition occurs after a prolonged treatment period, suggesting clinical inertia. Using data routinely available to integrated delivery networks and accountable care organizations, it is feasible to identify patients likely to qualify as treatment resistant. Monitoring risk factors may allow health systems to identify patients at risk for TRD earlier, potentially improving outcomes. Early identification of this at-risk population can allow for targeted resources for earlier intervention, more aggressive follow-up, and alternative treatment options. Furthermore, this model can be used to estimate future demand for specialized care resources, such as those delivered by mood disorder clinics. DISCLOSURES: This project was sponsored by Janssen Scientific Affairs. Pesa, Chow, and Verbanac are employed by Janssen Scientific Affairs and report stock ownership in Johnson & Johnson. Liberman, Davis, Heverly-Fitt, and Ruetsch are employed by Health Analytics, which received funding from Janssen Scientific Affairs for work on this project. This study was presented as a poster at the U.S. Psych Congress; October 3-6, 2019; San Diego, CA.

Development and Implementation of Clinical Outcome Measures for Automated Collection Within Specialty Pharmacy Practice.

BACKGROUND: Johns Hopkins Specialty Pharmacy Services recognized the need to identify and develop standardized collection methods for clinical outcome measures (COMs) to demonstrate program quality and value to third-party payers, manufacturers, and internal stakeholders. OBJECTIVE: To define specialty COMs and develop a framework for standardized data collection and reporting. METHODS: COMs for specialty pharmacy disease states (cystic fibrosis; hepatitis C; inflammatory conditions in dermatology, gastroenterology and rheumatology; and multiple sclerosis) were identified through a literature search, collaboration with specialty pharmacists, and committee review. Once identified, these measures were distributed to internal and external stakeholders that included specialty clinic team members, drug manufacturers, and third-party payers for input and validation. A standardized process for discrete documentation and data collection of these measures was implemented using case management software, electronic medical record integration, and informatics support. RESULTS: 28 COMs were identified. The various data sources used to collect the COMs were incorporated into an automated virtual dashboard to allow for regular review and sharing with clinicians, leadership, and other key stakeholders. The virtual dashboard included COMs with data derived from electronic medical records (n = 9), patient-reported outcomes based on responses to pharmacist-delivered questions (n = 11), and pharmacist assessment of outcomes (n = 8). The completed virtual dashboard was further refined to allow for reporting of both population and patient-level outcome results on a quarterly basis. CONCLUSIONS: This project describes methods to standardize documentation, data collection, and reporting of clinical outcomes data for multiple specialty conditions in a health system-integrated specialty pharmacy program. Through literature review and stakeholder consultation, a variety of potential COMs were identified for further evaluation of feasibility and value considering documentation and data collection requirements. Incorporation of COMs into a virtual dashboard will help facilitate the evaluation of program effectiveness, quality improvement planning, and sharing with stakeholders. Additional opportunities exist to further standardize COMs across the pharmacy industry to allow for future benchmarking and standardized evaluation of patient care programs. DISCLOSURES: No funding supported the writing of this article. The authors have no relevant conflicts of interest to disclose. This study was presented as a poster presentation at the APhA Annual Meeting, March 2018, Nashville, TN, and as a platform presentation at the Eastern States Conference, May 2018, Hershey, PA.

Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims.

BACKGROUND: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs. OBJECTIVES: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims. METHODS: We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman's coefficient (SC) after adjusting for age and sex. RESULTS: The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels. CONCLUSIONS: Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts. DISCLOSURES: No outside funding supported this study. The authors have nothing to disclose. The abstract of this work was presented at INFORMS Healthcare Conference, held on July 27-29, 2019, in Cambridge, MA.

Reconsidering hospital EHR adoption at the dawn of HITECH: implications of the reported 9% adoption of a "basic" EHR.

OBJECTIVE: In 2009, a prominent national report stated that 9% of US hospitals had adopted a "basic" electronic health record (EHR) system. This statistic was widely cited and became a memetic anchor point for EHR adoption at the dawn of HITECH. However, its calculation relies on specific treatment of the data; alternative approaches may have led to a different sense of US hospitals' EHR adoption and different subsequent public policy. MATERIALS AND METHODS: We reanalyzed the 2008 American Heart Association Information Technology supplement and complementary sources to produce a range of estimates of EHR adoption. Estimates included the mean and median number of EHR functionalities adopted, figures derived from an item response theory-based approach, and alternative estimates from the published literature. We then plotted an alternative definition of national progress toward hospital EHR adoption from 2008 to 2018. RESULTS: By 2008, 73% of hospitals had begun the transition to an EHR, and the majority of hospitals had adopted at least 6 of the 10 functionalities of a basic system. In the aggregate, national progress toward basic EHR adoption was 58% complete, and, when accounting for measurement error, we estimate that 30% of hospitals may have adopted a basic EHR. DISCUSSION: The approach used to develop the 9% figure resulted in an estimate at the extreme lower bound of what could be derived from the available data and likely did not reflect hospitals' overall progress in EHR adoption. CONCLUSION: The memetic 9% figure shaped nationwide thinking and policy making about EHR adoption; alternative representations of the data may have led to different policy.

Competency Assessment in Simulation of Electronic Health Records Tool Development.

Rapid deployment of electronic health records has resulted in a need for simulation centers to integrate the use of electronic health records into simulation-based learning activities within the clinical workflow. To assess the quality of the documentation in the electronic health record, the Competency Assessment in Simulation of Electronic Health Records Tool was developed. Lynn's method of content validity, combined with nominal group and Delphi techniques, was used to identify 15 domains of best practice in documentation. Participants with expertise in academic education, simulation, and informatics provided input into the development of the tool. The tool evolved over three rounds of Delphi that refined the language and provided anchors to promote accurate assessment of student and nurse documentation. The results of the Delphi narrowed the 15 domains down to 10 domains for scoring best practices in electronic documentation within simulation-based learning activities. The Competency Assessment in Simulation of Electronic Health Records Tool was developed to address the electronic health record competencies of both nursing students and practicing nurses in a simulation environment.

The Legacy of ARIES: Outdated Technology Interferes with HIV/AIDS Treatment.

Texas developed the AIDS Regional Information and Evaluation System in the early 1990s to administer federal grants from the then-new Ryan White program, which provides HIV- and AIDS-related health services for patients who can't get health care coverage or financial resources any other way. Physicians say the reservoir of medical data can be valuable, and without it, they can't get the funds they need to treat these patients. But the outdated reporting system - which still operates on 1990s technology - causes serious headaches and detracts from patient care.

[What place for French morbidity registries in the era of big data?] / Quelle place pour les registres de morbidité à l'ère des données massives de santé ?

The recent opening of massive health databases, as well as the development of methods and tools adapted to their data processing, questions the French model of "morbidity registry". In France in 2019, nearly 61 health registries were operating. As defined by law, these registries identify exhaustively all patients with a given disease in a given territory. Established several decades ago, these registries are part of the French surveillance system that is used for research and evaluation purposes. Since the advent of recent technological progress, large-scale databases are made available to researchers and it is possible with these databases to answer questions initially assigned to the registries. What is the place of such registries in this new context: are they obsolete or still useful? Should they be opposed to the new tools or are they complementary to them, and if so, what is their place in the new French public health ecosystem? The objective of this work was to assess the roles and missions of existing registries and to reflect on their positioning in this new environment. The French model of registry is sometimes questioned because of the complexity of its circuits, requiring a significant amount of human resources. However, the data that constitute them, validated by cross-checking information from several sources, are of very high quality, and make it possible to validate the data in the new databases (National Health Data System (NSDS) or Hospital Data Warehouses). Registries and new databases are in fact complementary, and far from jeopardizing this model, the recent opening of these databases represents an opportunity for registries to modernize their operations and respond to new missions.