An Insurer's Program To Incentivize Generic Oncology Drugs Did Not Alter Treatment Patterns Or Spending On Care.

The high and rising costs of anticancer drugs have received national attention. The prices of brand-name anticancer drugs often dwarf those of established generic drugs with similar efficacy. In 2007-16 UnitedHealthcare sought to encourage the use of several common low-cost generic anticancer drugs by offering providers a voluntary incentivized fee schedule with substantially higher generic drug payments (and profit margins), thereby increasing financial equivalence for providers in the choice between generic and brand-name drugs and regimens. We evaluated how this voluntary payment intervention affected treatment patterns and health care spending among enrollees with breast, lung, or colorectal cancer. We found that the incentivized fee schedule had neither significant nor meaningful effects on the use of incentivized generic drugs or on spending. Practices that adopted the incentivized fee schedule already had higher rates of generic anticancer drug use before switching, which demonstrates selection bias in take-up. Our study provides cautionary evidence of the limitations of voluntary payment reform initiatives in meaningfully affecting health care practice and spending.

Towards more Accessible Precision Medicine: Building a more Transferable Machine Learning Model to Support Prognostic Decisions for Micro- and Macrovascular Complications of Type 2 Diabetes Mellitus.

Although machine learning models are increasingly being developed for clinical decision support for patients with type 2 diabetes, the adoption of these models into clinical practice remains limited. Currently, machine learning (ML) models are being constructed on local healthcare systems and are validated internally with no expectation that they would validate externally and thus, are rarely transferrable to a different healthcare system. In this work, we aim to demonstrate that (1) even a complex ML model built on a national cohort can be transferred to two local healthcare systems, (2) while a model constructed on a local healthcare system's cohort is difficult to transfer; (3) we examine the impact of training cohort size on the transferability; and (4) we discuss criteria for external validity. We built a model using our previously published Multi-Task Learning-based methodology on a national cohort extracted from OptumLabs® Data Warehouse and transferred the model to two local healthcare systems (i.e., University of Minnesota Medical Center and Mayo Clinic) for external evaluation. The model remained valid when applied to the local patient populations and performed as well as locally constructed models (concordance: .73-.92), demonstrating transferability. The performance of the locally constructed models reduced substantially when applied to each other's healthcare system (concordance: .62-.90). We believe that our modeling approach, in which a model is learned from a national cohort and is externally validated, produces a transferable model, allowing patients at smaller healthcare systems to benefit from precision medicine.

Multi-Task Learning to Identify Outcome-Specific Risk Factors that Distinguish Individual Micro and Macrovascular Complications of Type 2 Diabetes.

Because deterioration in overall metabolic health underlies multiple complications of Type 2 Diabetes Mellitus, a substantial overlap among risk factors for the complications exists, and this makes the outcomes difficult to distinguish. We hypothesized each risk factor had two roles: describing the extent of deteriorating overall metabolic health and signaling a particular complication the patient is progressing towards. We aimed to examine feasibility of our proposed methodology that separates these two roles, thereby, improving interpretation of predictions and helping prioritize which complication to target first. To separate these two roles, we built models for six complications utilizing Multi-Task Learning-a machine learning technique for modeling multiple related outcomes by exploiting their commonality-in 80% of EHR data (N=9,793) from a university hospital and validated them in remaining 20% of the data. Additionally, we externally validated the models in claims and EHR data from the OptumLabs™ Data Warehouse (N=72,720). Our methodology successfully separated the two roles, revealing distinguishing outcome-specific risk factors without compromising predictive performance. We believe that our methodology has a great potential to generate more understandable thus actionable clinical information to make a more accurate and timely prognosis for the patients.

Discovering Public Health Nurse-Specific Family Home Visiting Intervention Patterns Using Visualization Techniques.

Visualization is a Big Data method for detecting and validating previously unknown and hidden patterns within large data sets. This study used visualization techniques to discover and test novel patterns in public health nurse (PHN)-client-risk-intervention-outcome relationships. To understand the mechanism underlying risk reduction among high risk mothers, data representing complex social interventions were visualized in a series of three steps, and analyzed with other important contextual factors using standard descriptive and inferential statistics. Overall, client risk decreased after clients received personally tailored PHN services. Clinically important and unique PHN-client-risk-intervention-outcome patterns were discovered through pattern detection using streamgraphs, heat maps, and parallel coordinates techniques. Statistical evaluation validated that PHN intervention tailoring leads to improved client outcomes. The study demonstrates the importance of exploring data to discover ways to improve care quality and client outcomes. Further research is needed to examine additional factors that may influence PHN-client-risk-intervention-outcome patterns, and to test these methods with other data sets.

Type 2 Diabetes Mellitus Trajectories and Associated Risks.

Disease progression models, statistical models that assess a patient's risk of diabetes progression, are popular tools in clinical practice for prevention and management of chronic conditions. Most, if not all, models currently in use are based on gold standard clinical trial data. The relatively small sample size available from clinical trial limits these models only considering the patient's state at the time of the assessment and ignoring the trajectory, the sequence of events, that led up to the state. Recent advances in the adoption of electronic health record (EHR) systems and the large sample size they contain have paved the way to build disease progression models that can take trajectories into account, leading to increasingly accurate and personalized assessment. To address these problems, we present a novel method to observe trajectories directly. We demonstrate the effectiveness of the proposed method by studying type 2 diabetes mellitus (T2DM) trajectories. Specifically, using EHR data for a large population-based cohort, we identified a typical trajectory that most people follow, which is a sequence of diseases from hyperlipidemia (HLD) to hypertension (HTN), impaired fasting glucose (IFG), and T2DM. In addition, we also show that patients who follow different trajectories can face significantly increased or decreased risk.

Public health nurse perceptions of Omaha System data visualization.

BACKGROUND: Electronic health records (EHRs) provide many benefits related to the storage, deployment, and retrieval of large amounts of patient data. However, EHRs have not fully met the need to reuse data for decision making on follow-up care plans. Visualization offers new ways to present health data, especially in EHRs. Well-designed data visualization allows clinicians to communicate information efficiently and effectively, contributing to improved interpretation of clinical data and better patient care monitoring and decision making. Public health nurse (PHN) perceptions of Omaha System data visualization prototypes for use in EHRs have not been evaluated. PURPOSE: To visualize PHN-generated Omaha System data and assess PHN perceptions regarding the visual validity, helpfulness, usefulness, and importance of the visualizations, including interactive functionality. METHODS: Time-oriented visualization for problems and outcomes and Matrix visualization for problems and interventions were developed using PHN-generated Omaha System data to help PHNs consume data and plan care at the point of care. Eleven PHNs evaluated prototype visualizations. RESULTS: Overall PHNs response to visualizations was positive, and feedback for improvement was provided. CONCLUSION: This study demonstrated the potential for using visualization techniques within EHRs to summarize Omaha System patient data for clinicians. Further research is needed to improve and refine these visualizations and assess the potential to incorporate visualizations within clinical EHRs.

Divisive Hierarchical Clustering towards Identifying Clinically Significant Pre-Diabetes Subpopulations.

Type 2 Diabetes Mellitus is a progressive disease with increased risk of developing serious complications. Identifying subpopulations and their relevant risk factors can contribute to the prevention and effective management of diabetes. We use a novel divisive hierarchical clustering technique to identify clinically interesting subpopulations in a large cohort of Olmsted County, MN residents. Our results show that our clustering algorithm successfully identified clinically interesting clusters consisting of patients with higher or lower risk of diabetes than the general population. The proposed algorithm offers fine control over the granularity of the clustering, has the ability to seamlessly discover and incorporate interactions among the risk factors, and can handle non-proportional hazards, as well. It has the potential to significantly impact clinical practice by recognizing patients with specific risk factors who may benefit from an alternative management approach potentially leading to the prevention of diabetes and its complications.