Abstract. From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.
Despite proper sleep hygiene being critical to our health, guidelines for improving sleep habits often focus on only a single component, namely, sleep duration. Recent works, however, have brought to light the importance of another aspect of sleep: bedtime regularity, given its ties to cognitive and metabolic health outcomes. To further our understanding of this often-neglected component of sleep, the objective of this work was to investigate the association between bedtime regularity and resting heart rate (RHR): an important biomarker for cardiovascular health. Utilizing Fitbit Charge HRs to measure bedtimes, sleep and RHR, 255,736 nights of data were collected from a cohort of 557 college students. We observed that going to bed even 30 minutes later than one's normal bedtime was associated with a significantly higher RHR throughout sleep (Coeff +0.18; 95% CI: +0.11, +0.26 bpm), persisting into the following day and converging with one's normal RHR in the early evening. Bedtimes of at least 1 hour earlier were also associated with significantly higher RHRs throughout sleep; however, they converged with one's normal rate by the end of the sleep session, not extending into the following day. These observations stress the importance of maintaining proper sleep habits, beyond sleep duration, as high variability in bedtimes may be detrimental to one's cardiovascular health.
On April 2nd, 2014, the Department of Health and Human Services (HHS) announced a historic policy in its effort to increase the transparency in the American healthcare system. The Center for Medicare and Medicaid Service (CMS) would publicly release a dataset containing information about the types of Medicare services, requested charges, and payments issued by providers across the country. In its release, HHS stated that the data would shed light on “Medicare fraud, waste, and abuse.” While this is most certainly true, we believe that it can provide so much more. Beyond the purely financial aspects of procedure charges and payments, the procedures themselves may provide us with additional information, not only about the Medicare population, but also about the physicians themselves. The procedures a physician performs are for the most part not novel, but rather recommended, observed, and studied. However, whether a physician decides on advocating a procedure is somewhat discretionary. Some patients require a clear course of action, while others may benefit from a variety of options. This article poses the following question: How does a physician's past experience in medical school shape his or her practicing decisions? This article aims to open the analysis into how data, such as the CMS Medicare release, can help further our understanding of knowledge transfer and how experiences during education can shape a physician's decision's over the course of his or her career. This work begins with an evaluation into similarities between medical school charges, procedures, and payments. It then details how schools' procedure choices may link them in other, more interesting ways. Finally, the article includes a geographic analysis of how medical school procedure payments and charges are distributed nationally, highlighting potential deviations.
The increasing availability of electronic health care records has provided remarkable progress in the field of population health. In particular the identification of disease risk factors has flourished under the surge of available data. Researchers can now access patient data across a broad range of demographics and geographic locations. Utilizing this Big healthcare data researchers have been able to empirically identify specific high-risk conditions found within differing populations. However to date the majority of studies approached the issue from the top down, focusing on the prevalence of specific diseases within a population. Through our work we demonstrate the power of addressing this issue bottom-up by identifying specifically which diseases are higher-risk for a specific population. In this work we demonstrate that network-based analysis can present a foundation to identify pairs of diagnoses that differentiate across population segments. We provide a case study highlighting differences between high and low income individuals in the United States. This work is particularly valuable when addressing population health management within resource-constrained environments such as community health programs where it can be used to provide insight and resource planning into targeted care for the population served.
Adverse drug reactions (ADRs) often go unreported or are inaccurately documented in the electronic medical recorded (EMR), even when they are severe and life‐threatening. Incomplete reporting can lead to future prescribing challenges and ADR reoccurrence. The aim of this study was to evaluate the documentation of ADRs within the EMR and determine specific factors associated with appropriate and timely ADR documentation. Retrospective data were collected from a pediatric hospital system ADR reports from October 2010 to November 2018. Data included implicated medication, type, and severity of reaction, treatment location, the presence or absence of ADR documentation in the EMR alert profile within 24 hours of the ADR hospital or clinic encounter discharge, ADR identification method, and the presence or absence of pharmacovigilance oversight at the facility where the ADR was treated. A linear regression model was applied to identify factors contributing to optimal ADR documentation. A total of 3065 ADRs requiring medical care were identified. Of these, 961 ADRs (31%) did not have appropriate documentation added to the EMR alert profile prior to discharge. ADRs were documented in the EMR 87% of the time with the presence of pharmacovigilance oversight and only 61% without prospective pharmacovigilance (P < .01). Severity of ADR was not a predictor of ADR documentation in the EMR, yet the implicated medication and location of treatment did impact reporting. An active pharmacovigilance service significantly improved pediatric ADR documentation. Further work is needed to assure timely, accurate ADR documentation.
The relationship between the Naranjo scaling system and pediatric adverse drug reactions (ADR) is poorly understood. We performed a retrospective review of 1,676 pediatric ADRs documented at our hospital from 2014–2018. We evaluated patient demographics, implicated medication, ADR severity, calculated Naranjo score, associated symptoms, and location within the hospital in which the ADR was documented. ADR severity was poorly correlated with Naranjo interpretation. Out of the 10 Naranjo scale questions, 4 had a response of “unknown” greater than 85% of the time. Cardiovascular and oncological/immunologic agents were more likely to have a probable or definite Naranjo interpretation compared to antimicrobials. Further strategies are needed to enhance the causality assessment of pediatric ADRs in clinical care.
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