Background The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. Objective This study aims to inform the feasibility of leveraging broad, statewide datasets for population health–driven decision-making by developing robust analytical models that predict COVID-19–related health care resource utilization across patients served by Indiana’s statewide Health Information Exchange. Methods We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). Results For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). Conclusions This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.
The primary objective of the COVID-19 Research Data Commons (CoRDaCo) is to provide broad and efficient access to a large corpus of clinical data related to COVID-19 in Indiana, facilitating research and discovery. This curated collection of data elements provides information on a significant portion of COVID-19 positive patients in the State from the beginning of the pandemic, as well as two years of health information prior its onset. CoRDaCo combines data from multiple sources, including clinical data from a large, regional health information exchange, clinical data repositories of two health systems, and state laboratory reporting and vital records, as well as geographic-based social variables. Clinical data cover information such as healthcare encounters, vital measurements, laboratory orders and results, medications, diagnoses, the Charlson Comorbidity Index and Pediatric Early Warning Score, COVID-19 vaccinations, mechanical ventilation, restraint use, intensive care unit and ICU and hospital lengths of stay, and mortality. Interested researchers can visit ridata.org or email askrds@regenstrief.org to discuss access to CoRDaCo.
Lay Summary Social factors, such as an individual’s housing, food, employment, and income situations, affect their overall health and well-being. As a result, data on patients’ social factors aid in clinical decision making, planning by hospital administrators and policy-makers, and enrich research studies with data representative of more factors influencing the life of an individual. Data on social factors can be collected at the time of a healthcare visit through screening questionnaires or are often documented in the clinical text as part of the social narrative. This study examines the use of natural language processing—a machine method to identify certain text within a larger document—to identify housing instability, financial insecurity, and unemployment from within the clinical notes. Using a relatively unsophisticated methodology, this study demonstrates strong performance in identifying these social factors, which will enable stakeholders to utilize these details in support of improved clinical care.
Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups. Here, we use machine learning to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended. We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout. Overall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.
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