Predictive Modeling Model: Improving Future Health Care

These objectives reflect a shift away from the traditional provider centered medical model, in which patients are often treated as passive recipients of care, and toward a more patient centered service model in which health decision making is expected to involve the active participation of the patient or consumer. In keeping with the Healthy People 2020 objectives and incorporating the Institute of Medicine’s goals for evidence-based care which emphasize patient centered care and respecting patients’ individual preferences (Institute of Medicine, 2001), the Health Outcomes Research Program at St. Luke’s Mid-American Heart Institute has developed a database with which patient-specific data can be used to estimate and model individual patient outcomes during clinical care. Decision aids created from these predictive risk estimates can

Forecasting is an important tool to help healthcare managers prepare for the challenges associated with rising health care costs. As the healthcare landscape continues to change, managers look at the past and present to predict the future. The U.S. government is major provider of health insurance for the elderly and disable persons. The government’s portion for covering healthcare costs has risen steadily, from 43% in 1980 and 38% in 1970 (Miller & Washington, 2006 p. 40). Medicare is the single largest source of payment for beneficiary health care costs; it covers about half of the cost of health care (Healthcare Financing Administration, 2006). The Affordable Care Act (ACA), which also provides medical coverage to low income persons, must also be factored into the cause and effect analysis. As a result of the changing landscape of health insurance, healthcare managers rely of analytical forecasting to predict future healthcare costs, examine cause and effect relationships and prepare their organization to provide quality affordable care to their patients.

Quality and care management innovations implemented by the Hill Physicians Medical Group were the use of health information technology (HIT), predictive modeling, and chronic care management. The Group capitalized $5.7 million towards the implementation of electronic medical records (EMRs) throughout their physician offices to assist with clinical workflow, management of patient health information (PHI), and integration evidence-based practice procedures. Predictive modeling was used to manage chronic conditions by utilizing a Priority Score to establish who might need major health care services. As such, nurse case managers were able to

The health care industry is one of the most dynamic and delicate industries in the U.S. having experienced healthy and substantial changes for the last thirty years most of which have aimed to improve health care management and services delivery to the patients. The changes have enabled the integration of technology into the industry such as in the area of informatics, science and research and payment services and clinical treatments. The health care sector has introduced various changes to address disease and health care management such as the Modernization Act of 2003, the Patient Protection Act and Affordable Act, which aim at improving health provision and most

Anthem with its state of the art Health Care Analytics engine and access to wide range of comprehensive information including complete claims history is able to provide doctors meaningful actionable information like high

These measurements include the assessment of risk factors[61], quality of care[62], diagnostic criteria[63], etc. Most of these studies used rule-based method[62, 63] to detect clearly defined and less complex (fewer expression variations) measurements, such as glucose level and body mass index. For some ambiguous and complex measurements, such as coronary artery disease and obesity status, machine learning plus external terminologies[61] are often

Through the statistical processing of data, healthcare organizations can identify potential relationships among data points and forecast future needs of the facility and its patients. This information can be used to identify key issues, solve problems, and improve patient outcomes through focused and effective care. In order to reap the benefits of healthcare informatics the data used must be of high quality. Data can come from a variety of sources, including clinical, inventory, financial, and

Optum, a specialized division of United Health Group, provides predictive analytics coupled with technology-enabled clinical services, with a goal to improve the patient experience and member outcomes in a cost-efficient manner (United Health Group [UHG], 2017). The mission statement and vision “ To help people live healthier lives, and, to help make the health system work better for everyone” (Our Mission, para.1) is what drives the work done at Optum (Optum, n.d.). One of the many clinical areas within Optum is the clinical claim review and resolution team which works to resolve provider disputes related to claim payment and authorization of services.

Clinicians in intensive care units (ICUs) rely on standardized scores as risk prediction models to predict a patient's vulnerability to life-threatening events. Current scales calculate scores from a fixed set of conditions collected within a specific time window. However, modern monitoring technologies generate complex, temporal, and multimodal patient data that conventional prediction scales cannot fully utilize. Thus, a more sophisticated model is needed to tailor individual characteristics and incorporate multiple temporal modalities for a personalized risk prediction. Furthermore, most scales focus on adult patients. To address this need, we propose a new ICU risk prediction system, called icuARM-II, using a large-scaled pediatric ICU

Health forecasting is a novel region of forecasting, and an important apparatus for foreseeing future health occasions or circumstances, for example, requests for health services and social insurance needs. It encourages preventive pharmaceutical and social insurance intercession systems, by pre-illuminating health service suppliers to take suitable alleviating activities to minimize chances and oversee request. Health forecasting requires reliable information, data and appropriate logical instruments for the expectation of particular health conditions or circumstances.

Kim, K, Changhyiok, L. O’Leary, K. Rosenauer, S. Mehrotra, 2014. Predicting Patient Volumes in Hospital Medicine. Retrieved November 1, 2017 from

Change is not only challenging to achieve, it’s difficult to duplicate-- which is why Castlight’s Director of Product, Alka Tandon, implemented the BJ Fogg Model into their platform almost 2 years ago. “The goal for the product is to change people's ability to make better healthcare decisions using predictive analytics, combined with personalized content that makes people motivated to change their behavior,”

Our approach to classification of user’s cancer risks will use a larger number of dimensions to calculate risk of different types of cancers than other similar analytic techniques. The data used for this project will come from the Surveillance, Epidemiology, and End Results (SEER) data from the National Cancer Institute. SEER's extensive datasets allow many different analyses to be done from general population cancer statistics (Siegel) to specific medical decision making applications such as whether a certain treatment for prostate cancer would be beneficial (Culp). Other approaches have utilized SEER datasets and supervised classification methods to develop survival prediction models for colon cancer (Al-Bahrani) and chart survival curves for different treatments for lung cancer (Owonikoko). However, these previous approaches have focused on one type of cancer and are therefore less comprehensive than the tool we are seeking to develop. We also plan to implement an interactive, user-friendly visualization tool that allows for quick interpretation of the results. The user would receive a list of cancer they are most

In Chapter 2, four machine learning classifiers are used to find the likelihood of having BRCA mutation based on detailed personal and family history of cancer information. The data used for validation of the models emerges from a recent nation-wide survey study (ABOUT) of those who requested BRCA genetic testing through one of the commercial health insurance companies in the United States. This is the first study evaluating existing well-known BRCA risk estimation models using data on general population in the United States. The models considered were gradient boosting model (GBM), random forest, support vector machines, and

Forecasting is often defined as the estimation of the value of a variable (or set of variables) at some future point in time (Goodier, 2010). It can be applied to a number of different situations when there is uncertainty about the future and the data collected can aid in decisions that need to be made (Armstrong, 2001). In relation to healthcare, forecasting models have been used to aid their sector’s departments to plan staff rota schedules, ensuring that a sufficient amount of senior staff are available at any given time throughout the day, week, month and year. As explained previously, a fundamental factor that causes overcrowding is a limited supply of resources to treat patients, leading to a longer time spent in an Emergency