Electronic Health Records Communication among Team Members and Quality of Care and Costs for Patients with Cardiovascular Disease in Primary Care

This study determines how changes in electronic health record (EHR) communication patterns in primary care teams are related to quality of care and costs for patients with cardiovascular disease. Counts of EHR messages routed between any two team members were extracted from the EHR system, and flow betweenness, the proportion of information passed indirectly within the team, was calculated. The analysis related changes in team flow betweenness to changes in acute care visits and associated medical costs for the teams’ patients with cardiovascular disease. The results indicated that patient hospital visits increased by 7% (SE 3%) for every 1% increase in team EHR flow betweenness. Medical costs increased by $141 (SE$67) per patient for every 1% increase in team EHR flow betweenness. EHR team communication flow patterns may be an important avenue to explore for raising quality of care and lowering costs for primary care patients with cardiovascular disease

Depressive and psychotic symptoms in schizophrenia:Focus on networks and treatment

This thesis has two main aims. First, to review and increase knowledge concerning symptom interaction in patients with schizophrenia, with a specific focus on co-occurring depressive symptoms and its neural correlates in major depressive disorder (Part I and II). Second, to review and investigate different treatment aspects and outcomes in schizophrenia (quality of life, depressive symptoms and mortality) (Part III). In sum, both network studies showed the importance of depressive symptoms in the symptom networks of patients with schizophrenia and showed the stability of such a network structure. Although the network approaches has several issues of debate, it is a promising new way of thinking about psychopathology. The network approach is an example of a new conceptualisation of psychopathology as dynamic systems that change over time. Additionally, this view on mental illness facilitates a more transdiagnostic approach, in which emotion regulation should be an important target for future studies. Given the frequent co-occurrence of depressive symptoms in patients with schizophrenia, its centrality, its correlations with suicidality and influence on quality of life, it is highly important to adequately treat co-occurring depressive symptoms and episodes. Systematically following the provided treatment guide to treat depressive symptoms or episodes might be useful. Additionally, meta-analyses showed that schizophrenia patients who do not use antipsychotics have a higher mortality risk compared to patients that use antipsychotics. In a similar way, continuous use of clozapine was related to a lower mortality risk compared to patients using other antipsychotics

Estimates and predictors of health care costs of esophageal adenocarcinoma : A population-based cohort study

Background: Esophageal adenocarcinoma (EAC) incidence is increasing rapidly. Esophageal cancer has the second lowest 5-year survival rate of people diagnosed with cancer in Canada. Given the poor survival and the potential for further increases in incidence, phase-specific cost estimates constitute an important input for economic evaluation of prevention, screening, and treatment interventions. The study aims to estimate phase-specific net direct medical costs of care attributable to EAC, costs stratified by cancer stage and treatment, and predictors of total net costs of care for EAC. Methods: A population-based retrospective cohort study was conducted using Ontario Cancer Registry-linked administrative health data from 2003 to 2011. The mean net costs of EAC care per 30 patient-days (2016 CAD) were estimated from the payer perspective using phase of care approach and generalized estimating equations. Predictors of net cost by phase of care were based on a generalized estimating equations model with a logarithmic link and gamma distribution adjusting for sociodemographic and clinical factors. Results: The mean net costs of EAC care per 30 patient-days were $1016 (95$955-$1078) in the initial phase,$669 (95% CI, $594-$743) in the continuing care phase, and $8678 (95$8217-$9139) in the terminal phase. Overall, stage IV at diagnosis and surgery plus radiotherapy for EAC incurred the highest cost, particularly in the terminal phase. Strong predictors of higher net costs were receipt of chemotherapy plus radiotherapy, surgery plus chemotherapy, radiotherapy alone, surgery alone, and chemotherapy alone in the initial and continuing care phases, stage III-IV disease and patients diagnosed with EAC later in a calendar year (2007-2011) in the initial and terminal phases, comorbidity in the continuing care phase, and older age at diagnosis (70-74 years), and geographic region in the terminal phase. Conclusions: Costs of care vary by phase of care, stage at diagnosis, and type of treatment for EAC. These cost estimates provide information to guide future resource allocation decisions, and clinical and policy interventions to reduce the burden of EAC

Ensemble Risk Model of Emergency Admissions (ERMER)

Introduction About half of hospital readmissions can be avoided with preventive interventions. Developing decision support tools for identification of patients’ emergency readmission risk is an important area of research. Because, it remains unclear how to design features and develop predictive models that can adjust continuously to a fast-changing healthcare system and population characteristics. The objective of this study was to develop a generic ensemble Bayesian risk model of emergency readmission. Methods We produced a decision support tool that predicts risk of emergency readmission using England's Hospital Episode Statistics inpatient database. Firstly, we used a framework to develop an optimal set of features. Then, a combination of Bayes Point Machine (BPM) models for different cohorts was considered to create an optimised ensemble model, which is stronger than the individual generative and non-linear classifications. The developed Ensemble Risk Model of Emergency Admissions (ERMER) was trained and tested using three time-frames: 1999-2004, 2000-05 and 2004-09, each of which includes about 20% of patients in England during the trigger year. Results Comparisons are made for different time-frames, sub-populations, risk cut-offs, risk bands and top risk segments. The precision was 71.6% to 73.9%, the specificity was 88.3% to 91.7% and the sensitivity was 42.1% to 49.2% across different time-frames. Moreover, the Area Under the Curve was 75.9% to 77.1%. Conclusions The decision support tool performed considerably better than the previous modelling approaches, and it was robust and stable with high precision. Moreover, the framework and the Bayesian model allow the model to continuously adjust it to new significant features, different population characteristics and changes in the system

A risk score to predict the development of hepatic encephalopathy in a populationâ based cohort of patients with cirrhosis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146442/1/hep29628_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146442/2/hep29628-sup-0001-suppinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146442/3/hep29628.pd

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-