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.

Acetylcarnitine Is Associated With Cardiovascular Disease Risk in Type 2 Diabetes Mellitus.

Objective: This study aimed to identify the association between specific short-chain acylcarnitines and cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM). Method: We retrieved 1,032 consecutive patients with T2DM who meet the inclusion and exclusion criteria from the same tertiary care center and extracted clinical information from electronic medical records from May 2015 to August 2016. A total of 356 T2DM patients with CVD and 676 T2DM patients without CVD were recruited. Venous blood samples were collected by finger puncture after 8 h fasting and stored as dried blood spots. Restricted cubic spline (RCS) analysis nested in binary logistic regression was used to identify possible cutoff points and obtain the odds ratios (ORs) and 95% confidence intervals (CIs) of short-chain acylcarnitines for CVD risk in T2DM. The Ryan-Holm step-down Bonferroni procedure was performed to adjust p-values. Stepwise forward selection was performed to estimate the effects of acylcarnitines on CVD risk. Result: The levels of C2, C4, and C6 were elevated and C5-OH was decreased in T2DM patients with CVD. Notably, only elevated C2 was still associated with increased CVD inT2DM after adjusting for potential confounders in the multivariable model (OR = 1.558, 95%CI = 1.124-2.159, p = 0.008). Furthermore, the association was independent of previous adjusted demographic and clinical factors after stepwise forward selection (OR = 1.562, 95%CI = 1.132-2.154, p = 0.007). Conclusions: Elevated C2 was associated with increased CVD risk in T2DM.

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.

Healthcare Providers' Intention to Use Technology to Attend to Clients in Cape Coast Teaching Hospital, Ghana.

BACKGROUND: Patient records' relevance is associated with a variety of needs and objectives. Substantiating the health of patients perpetually and allowing professionals in the medical field to assess both signs and symptoms that fall in a relatively wider temporal point of view and contributions that lead to enhanced diagnoses and treatment are all quintessential of patient records. The advancement of information technology systems has led to the anticipation that development will be put into digitization and electronic means of storing patient records in order to grease their handling. Cape Coast Teaching Hospital (CCTH) is piloting implementation of patient's electronic health record system. The introduction of the electronic health record system known as Lightwave Hospital Information Management System (LHIMS) was to provide a permanent solution to patients' continuity of care. User's acceptance of new information technology is seen to be one of the most challenging issues in information system. This study assesses healthcare providers' (HP') behavioural intention to use LHIMS to attend to clients in Cape Coast Teaching Hospital and other factors influencing it. METHODS: A nonexperimental cross-sectional study was used to obtain information from 84 HP recruited from the various departments and units in CCTH who use LHIMS to attend to clients. The sample size of 90, representing 8% of HP in CCTH, was randomly selected from the various departments and units. However, 84 (indicating 93.3% response rate) of the selected HP were available during the period of the research. RESULTS: Perceived ease of use (PEOU) of LHIMS had the strongest direct effect on perceived usefulness (PU), with a highly significant path coefficient of 0.75. PU had the greatest impact on attitude about HP' behavioural intention to use (BIU) LHIMS to attend to patients' healthcare delivery in CCTH (0.91). This relationship was highly significant at p < 0.001. PEOU did not have a significant direct effect on attitude about LHIMS use, as hypothesized in the original technology acceptance model. However, attitude towards use had a strong significant effect on HP' BIU of LHIMS, with a strong statistically significant path coefficient of 0.98 at p < 0.001. CONCLUSIONS: We conclude that attitude towards use have a significant influence on HP' behavioural intention to use LHIMS to attend to clients in Cape Coast Teaching Hospital.

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.

Increased Utilization of Virtual Visits and Electronic Approaches in Clinical Research During the COVID-19 Pandemic and Thereafter.

OBJECTIVES: To assess the impact of the COVID-19 pandemic on clinical research and the use of electronic approaches to mitigate this impact. METHODS: We compared the utilization of electronic consenting, remote visits, and remote monitoring by study monitors in all research studies conducted at Mayo Clinic sites (Arizona, Florida, and Minnesota) before and during the COVID-19 pandemic (ie, between May 1, 2019 and December 31, 2020). Participants are consented through a participant-tracking system linked to the electronic health record. RESULTS: Between May 2019, and December 2020, there were 130,800 new consents across every modality (electronic and paper) to participate in a non-trial (107,176 [82%]) or a clinical trial (23,624 [18%]). New consents declined from 5741 in February 2020 to 913 in April 2020 but increased to 11,864 in November 2020. The mean (standard deviation [SD]) proportion of electronic consent increased from 22 (2%) before to 45 (20%) during the pandemic (P=.001). Mean (SD) remote electronic consenting increased from 0.3 (0.5%) to 29 (21%) (P<.001). The mean (SD) number of patients with virtual visits increased from 3.5 (2.4%) to 172 (135%) (P=.003) per month between pre-COVID (July 2019 to February 2020) and post-COVID (March to December 2020) periods. Virtual visits used telemedicine (68%) or video (32%). Requests for remote monitor access to complete visits increased from 44 (17%) per month between May 2019 and February 2020 to 111 (74%) per month between March and December 2020 (P=.10). CONCLUSION: After a sharp early decline, the enrollment of new participants and ongoing study visits recovered during the COVID-19 pandemic. This recovery was accompanied by the increased use of electronic tools.

Medical phenome of musicians: an investigation of health records collected on 9803 musically active individuals.

Previous studies suggest that musicians may be at higher risk for a set of medical problems; however, this literature has been limited by relatively small sample sizes, self-reports, and lack of controls. To address such limitations, we examined trends in the medical care of musicians in an Electronic Health Record database. On the basis of a collection of keywords and regular expressions in the patients' clinical notes, we identified 9803 "musicians" that we matched for sex, median age (across the medical record), ethnicity, race, the length of record, and the number of visits with 49,015 controls. We fitted 1263 logistic regression models to determine whether the phenotype was correlated with musicianship. Two hundred fifty-seven phenotypes were more prevalent in musicians than controls after Bonferroni adjustment (P < 7.6 × 10-6 ), including diseases of the larynx and vocal cords (OR = 2.32 (95% CI: 2.25-2.40)), and hearing loss (OR = 1.36 (95% CI: 1.32-1.39)). Fifteen phenotypes were significantly more prevalent in controls than musicians, including coronary atherosclerosis (OR = 0.91 (95% CI: 0.89-0.94)). Although being a musician was related to many occupational health problems, we identified protective effects of musicianship in which certain disorders were less common in musicians than in controls, indicating that active musical engagement could have health benefits analogous to athletic engagement.

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.

Building a semantically annotated corpus for chronic disease complications using two document types.

Narrative information in electronic health records (EHRs) contains a wealth of information related to patient health conditions. In addition, people use Twitter to express their experiences regarding personal health issues, such as medical complaints, symptoms, treatments, lifestyle, and other factors. Both genres of text include different types of health-related information concerning disease complications and risk factors. Knowing detailed information about controlling disease risk factors has a great impact on modifying these risks and subsequently preventing disease complications. Text-mining tools provide efficient solutions to extract and integrate vital information related to disease complications hidden in the large volume of the narrative text. However, the development of text-mining tools depends on the availability of an annotated corpus. In response, we have developed the PrevComp corpus, which is annotated with information relevant to the identification of disease complications, underlying risk factors, and prevention measures, in the context of the interaction between hypertension and diabetes. The corpus is unique and novel in terms of the very specific topic in the biomedical domain and as an integration of information from both EHRs and tweets collected from Twitter. The annotation scheme was designed with guidance by a domain expert, and two further domain experts performed the annotation, resulting in a high-quality annotation, with agreement rate F-scores as high as 0.60 and 0.75 for EHRs and tweets, respectively.

A retrospective look at the predictions and recommendations from the 2009 AMIA policy meeting: did we see EHR-related clinician burnout coming?

Clinicians often attribute much of their burnout experience to use of the electronic health record, the adoption of which was greatly accelerated by the Health Information Technology for Economic and Clinical Health Act of 2009. That same year, AMIA's Policy Meeting focused on possible unintended consequences associated with rapid implementation of electronic health records, generating 17 potential consequences and 15 recommendations to address them. At the 2020 annual meeting of the American College of Medical Informatics (ACMI), ACMI fellows participated in a modified Delphi process to assess the accuracy of the 2009 predictions and the response to the recommendations. Among the findings, the fellows concluded that the degree of clinician burnout and its contributing factors, such as increased documentation requirements, were significantly underestimated. Conversely, problems related to identify theft and fraud were overestimated. Only 3 of the 15 recommendations were adjudged more than half-addressed.