New Payment Models and Big Data Analytics

New Payment Models and Big Data Analytics

Avnish Rastogi (Oracle Corp, USA)
Copyright: © 2014 |Pages: 15
DOI: 10.4018/978-1-4666-5202-6.ch145
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Abstract

It has been fascinating to watch how American healthcare delivery system is going through a paradigm shift to meet new government mandates and bend the care delivery cost curve. For years, US health care system has been fragmented and falling short on quality, outcomes, costs, and lacking framework to support care continuum. According to the study conducted by National Research Council and the Institute of Medicine, for many years, US population has been dying at the younger age than population with similar characteristics in other countries such as Canada, Australia and Japan. When compared with the peer countries, US Population did worse in health areas such as drug related deaths, obesity, chronic diseases, disability, etc. (Institute of Medicine of the National Academics. 2013 in, U.S Health in International Perspective Shorter Lives, Poorer Health.)
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Introduction

In our fragmented healthcare system, services are variable and lacking standardization in care treatment with limited oversight. Most physicians practice in small group with either no electronic medical record (EMR) or an EMR with limited capabilities and limited or no connectivity between the places of services. Future healthcare delivery system should be more patient centric, coordinated, compassionate and affordable. Moreover, a system that consistently delivers reliable performance and experiences improvements with each care delivery and transition. The healthcare reform is shifting care delivery focus from volume based provider centric organization to value based patient centric organization. However, change will require sophisticated IT infrastructure to facilitate the successful transition. This chapter will discuss how healthcare reform is driving new innovations in IT Infrastructure to encourage better care, better quality and lower cost for the entire US population. Moreover, discuss how these reforms are impacting healthcare organizations and role of technologies such HIE (Health Information Exchange) and big data analytics in meeting these mandates. This chapter will cover below topics:

  • Healthcare and Payment Model Reforms

  • What is Data Warehouse/Big Data Analytics?

  • What is healthcare information exchange?

  • Conclusion

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Background

It’s not news to anybody how much we spend on healthcare delivery, but still a number of the uninsured population in America is continuously rising. Healthcare industry has been struggling with rising cost in care delivery because the existing system is driven by service volume rather than care outcome or quality of care. The Center for Medicare and Medicaid (CMS) estimates total healthcare spending will nearly double from $2.6 trillion in 2010 to $4.6 trillion in 2020, and healthcare spending per capita is forecasted to increase $13,708 in 2020 from $8327 in 2010. The report shows that the biggest jump in the spending will occur in next couple of years when reform is fully implemented.

Payment model for the current healthcare system except Healthcare Maintenance Organization (HMO) is setup based on the Fee for Service (FFS) model, under which physicians or hospitals are reimbursed based on the care services or the procedures performed on a patient. However, under this model, quality of the care delivery or reduce healthcare cost is the least priority in care providers’ mind. The model has promoted lack of accountability in the care delivery stakeholder to deliver patient care at affordable cost which resulted into creating an unsustainable growth in the healthcare cost.

Multiple factors account for the unsustainable growth in healthcare spending. Healthcare outpatient services and physician/clinical services account for more than half of the nation’s health expenditures. New technology adoption and prescription drugs have been cited as major contributor to the increase in the overall healthcare spending. The fee for service (FFS) model has been viewed as a major barrier in achieving quality and cost effective health care delivery. The model promotes behavior which rewards the overuse of services resulting in increased healthcare cost without ANY additional perceived value to the patients. It doesn’t support the culture to prevent hospital admission (or readmission), duplicate procedures, or care coordination.

Key Terms in this Chapter

ACO: Centers for Medicare & Medicaid Services (CMS) defines Accountable Care Organizations (ACOs) as groups of doctors, hospitals, and other health care providers, who come together voluntarily to give coordinated high quality care to their Medicare patients.

Predictive Analytics: Predictive analytics requires new methods and technologies by an organization to mine data to discover trends/patterns and test large numbers variables for unexpected insight.

Fee for Service (FFS): FFS is a payment model which promotes more treatment because a provider’s payment is dependent on the quantity of care rather than the quality of care. The model promotes behavior which doesn’t encourage coordinated care.

Meaningful Use: Objectives established by the center of Medicare and Medicaid (CMS) for “meaningful use” that eligible professionals, eligible hospitals, and critical access hospitals (CAHs) must meet in order to receive an incentive payment. To receive an EHR incentive payment, providers have to show that they are “meaningfully using” their EHRs by meeting thresholds for a number of objectives.

Population Management: Population management, as defined by the Disease Management Association of America, is “a system of coordinated healthcare interventions and communications for populations with conditions in which patient self-care efforts are significant.” Population management focuses on the following: dentifying the similar risk factors affecting a group, determining what steps can be taken to improve the health of a group, identifying preventive care intervention, analyzing aggregated patient data to assist in the clinical decision-making process, using preferred patient communication to follow up with patients.

HIE: The mobilization of healthcare information electronically across organizations within a region or community. HIE provides the capability to electronically move clinical information between disparate healthcare information systems, while maintaining the meaning of the information being exchanged. The goal of HIE is to facilitate access to, and retrieval of, clinical data to provide safer, more timely, efficient, effective, equitable, and patient-centered care.

Big Data: Gartner defines Big data as high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization (The importance of Big Data: A Definition. Douglas L).

HMO: HealthCare.Gov defines HOM as a type of health insurance plan that usually limits coverage to care from doctors who work for or contract with the HMO. It generally won't cover out-of-network care except in an emergency. An HMO may require you to live or work in its service area to be eligible for coverage. HMOs often provide integrated care and focus on prevention and wellness.

Fee for Performance (FFP): A payment model which foster greater accountability and quality based outcome.

Hadoop Common: A common set of libraries to support other hadoop modules.

Bundle Payment: American Medical Association (AMA) defined bundle payment as a single “bundled” payment that covers services delivered by two or more providers during a single episode of care or over a specific period of time.

Unstructured Data: Gartner Inc. defines unstructured data as “information not stored in the database or file system as discrete data.” In a healthcare EMR system, this data generally refers to test results, referrals, progress notes, transition summary, H&P, etc.

Hadoop Map Reduce: A hadoop system for parallel processing of the large data sets.

Health Risk Assessment (HRA): HRA is a software solution designed to facilitate Annual Wellness Visit to encourage individuals to take an active role in accurately assessing and managing their health, and consequently improve their well-being and quality of life. The annual wellness visit includes reviewing patient medical history, preventive medical screening schedule and personalize medical plan to promote healthy lifestyle. The approach refocusing on an individual’s active role in health care is accomplished by evaluating beneficiaries’ current health and wellness behaviors, followed by advice and counsel on ways to become healthier and remain healthy for as long as possible. The tools available to the practitioner to accomplish this purpose include administering an easy-to-use HRA with feedback, along with providing credible information, advice, resources, and support that will raise patients’ awareness of their individual health issues, promote self-reliance and self-care, prompt active decision-making, and increase confidence to manage one’s health. (Center for Disease Control and Prevention: A Framework for Patient-Centered Health Risk Assessments)

CCD: Health Information Technology Standards Panel (HITSP) defines CCD as a document summarizing a patient’s medical status for the purpose of information exchange. The contents in a CCD document may include both administrative (registration, demographics, insurance, etc.) and clinical (problem list, medication list, allergies, test results, etc.) information.

Hadoop Distributed File System (HDFS): A files system to support large files storage across the distributed file system.

Hadoop: Hadoop is a framework that allows the distributed processing of large data sets across of hundreds or thousands of low-cost small computers using simple programming models. It is designed to scale up horizontally from a single node cluster to multi-node cluster ( http://hadoop.apache.org/ ). The framework includes:

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