The goal of this study was to analyze diagnostic discrepancies between emergency department (ED) and hospital discharge diagnoses in patients with congestive heart failure admitted to the ED. Using a ...synthetic dataset from the Department of Veterans Affairs, the patients' primary diagnoses were compared at two levels: diagnostic category and body system. With 12,621 patients and 24,235 admission cases, the study found a 58% mismatch rate at the category level, which was reduced to 30% at the body system level. Diagnostic categories associated with higher levels of mismatch included aplastic anemia, pneumonia, and bacterial infections. In contrast, diagnostic categories associated with lower levels of mismatch included alcohol-related disorders, COVID-19, cardiac dysrhythmias, and gastrointestinal hemorrhage. Further investigation revealed that diagnostic mismatches are associated with longer hospital stays and higher mortality rates. These findings highlight the importance of reducing diagnostic uncertainty, particularly in specific diagnostic categories and body systems, to improve patient care following ED admission.
Summary
There has been a call for evidence‐based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of ...up‐to‐date evidence, the limited integrative analytical data sets, and the slow translations to routine care delivery. Overcoming these issues requires knowledge discovery pipelines based on big data and health analytics, intelligent integrative informatics approaches, and learning health systems. This article examines how this can be accomplished by utilizing big data. These data can be gathered from four major streams: patients, clinical data, biological data, and normative data sets. All these must then be uniformly combined for analysis and modelling and the meaningful findings can be implemented clinically. By executing data capture cycles and integrating the subsequent findings, practitioners are able to improve public oral health and care delivery.
Quasi-experimental study designs, often described as nonrandomized, pre-post intervention studies, are common in the medical informatics literature. Yet little has been written about the benefits and ...limitations of the quasi-experimental approach as applied to informatics studies. This paper outlines a relative hierarchy and nomenclature of quasi-experimental study designs that is applicable to medical informatics intervention studies. In addition, the authors performed a systematic review of two medical informatics journals, the Journal of the American Medical Informatics Association (JAMIA) and the International Journal of Medical Informatics (IJMI), to determine the number of quasi-experimental studies published and how the studies are classified on the above-mentioned relative hierarchy. They hope that future medical informatics studies will implement higher level quasi-experimental study designs that yield more convincing evidence for causal links between medical informatics interventions and outcomes.
To effectively develop patient-centered interfaces and functionality, it is essential to investigate different viewpoints on pulmonary telerehabilitation. The purpose of this study is to explore the ...views and experiences of COPD patients after the completion of a 12-month home-based pulmonary telerehabilitation program. Semi-structured qualitative interviews were conducted with 15 COPD patients. The interviews were analyzed using a thematic analysis approach to deductively identify patterns and themes. Patients responded with approval for the telerehabilitation system, particularly for its convenience and ease of use. This study offers a thorough investigation of patient viewpoints when utilizing the telerehabilitation technology. These insightful observations will be considered for future development and implementation of a patient-centered COPD telerehabilitation system to provide support tailored to patient needs, preferences, and expectations.
Potential of big data analytics in analyzing outcomes of opioid treatment programs (OTP) has not been fully explored. The goal of this study was to assess potential of big data in predicting OTP ...outcomes based on the initial intake forms which includes demographics, social and health history. The analytical sample comprised over 30,000 people admitted in OTP. Around 66% of patients reported improvements after completing OTP. We compared the results of Logistics Regression, Random Forest, and XGBoost for predictive modeling. XGBoost with sampling and threshold tuning performed the best (44% F1 score) with over 60% accuracy. Further big data exploration of OTP is warranted.
Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time ...even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.
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