Disclosure of Commercial Interests

Transcription

1 Disclosure of Commercial Interests I have commercial interests in the following organization: Dr. David G. Wolf, Assoc.Professor of Health Services Administration Barry University, Miami, FL We are a private, 4-year Catholic University

2 Using Analytics for Quality & Financial Improvement: An Administrator's Perspective David G. Wolf, Ph.D., CNHA, CALA, CAS, Fellow ACHCA Associate Professor, Healthcare Administration Barry University Miami Shores, FL 11/17/17

3 Certified Nursing Home, Assisted Living and Subacute Care Administrator Associate Professor of Health Services Administration Long-Term Care Leadership Researcher Academic Board Member ACHCA Vice Chair Long Term Care Program - AUPHA Co-Creator of the Master s Degree in Health Care informatics Certified Nursing Home, Assisted Living and Subacute Care Administrator Licensed in New Jersey and Florida

4 Learning Objectives: By The End of this session you should be able to: Describe and create a workflow process demonstrating how information technology influences various contemporary quality improvement activities in long-term care. Demonstrate an understanding of the role of information technology in organizational quality improvement efforts within the long-term care setting. Assess the importance of various organizational contextual factors and benchmarks of best practices in health care quality management/improvement processes.

5 Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future. Let s start with the Terms Informatics broadly describes the study and practice of creating, storing, finding, manipulating and sharing information. The role of an informaticist is to facilitate the development of systems and processes which enable questions to be answered from available data. Health data analytics, also known as clinical data analytics, involves the EXTRACTION of actionable insights from sets of data, typically collected from electronic health records (EHRs), billing systems, existing internal data and external sources.

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7 One more term Benchmarking Benchmarking--the process of establishing a standard of excellence and comparing a business function or activity, a product, or an enterprise as a whole with that standard--will be used increasingly by healthcare institutions to reduce expenses and simultaneously improve product and service quality. As a component of total quality management, benchmarking is a continuous process by which an organization can measure and compare its own processes with those of organizations that are leaders in a particular area.

8 Why I am I here today? According to the University of South Florida website, The changing landscape of healthcare is creating a huge demand for health data analytics. According to a recent Research and Markets report, health data analytics is poised to grow into a $34.27 billion industry by the end of A 2013 report by the U.S. Commission on Long-Term Care estimates that the number of people who are dependent on longterm care is expected to rise from 12 million in 2010 to 27 million in Analytics will be a critical function to support the treatment and tracking of these patients from patient care and operational standpoints

9 Why I am I here today? BUT.the reality in healthcare is that we are just beginning to have the necessary analytics capabilities that enable system-wide quality improvement and cost reduction efforts. The real promise of analytics lies in its ability to transform healthcare into a truly data-driven culture.

10 After years of dwelling in the shadows of healthcare, the long-term and post-acute care industry may finally be ready to join its hospital colleagues in the IT spotlight. The path is long and steep, but operators of skilled nursing, outpatient rehabilitation, assisted living, memory care, hospice and home care agencies are embracing their important new roles as providers in the dynamic post-acute care environment. Where are we with Analytics and I/T?

11 When the Office of the National Coordinator put together the electronic health record and interoperability initiative in 2004, long-term care got nary a mention; and as recently as 2009, LTC providers got left out of the multibillion dollar incentive from the American Recovery and Rehabilitation Act because designers didn't consider their relevance for the program. Where are we with Analytics and I/T?

12 Where are we with Analytics and I/T? How times have changed in just a short period of time. With the advent of accountable care organizations, post-acute care provider networks and the move toward population health, suddenly longterm care facilities have gained prominence as valuable components in the equation.

13 Where are we with Analytics and I/T? But, while they now have a higher profile, long-term care operators are also coming to terms with the fact that they are still largely dependent on manual processes and that they are woefully deficient in IT personnel. In essence, this new role comes with the huge responsibility of joining the digital revolution.

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16 How Can We Use Analytics To Help Us in LT/PAC Implementing predictive analytics software and benchmarked data analysis tools can ensure that the medical information is accurately recorded over a long period of time and ultimately produces better results. Nursing facilities can conduct a management team effort to help familiarize workers with the transition of using these software methods on a consistent basis. Skilled nursing facilities that focus heavily on MDS accuracy, identifying and documenting high-risk residents and keeping detailed records of patient history are likely to demonstrate their excellence and avoid any potential revoking of the right to receive payments from Medicare and Medicaid.

17 How Can We Use Analytics To Help Us in LT/PAC One of the best ways for skilled nursing facilities to achieve a high-star rating is to provide the most accurate resident assessment data possible, then use this data to predict and prevent negative outcomes. The implementation of predictive analytics can help the staff of nursing home facilities make better informed decisions on resident treatment and cut out time loss and wasteful expenses generated by bad data.

18 How Can We Use Analytics To Help Us in LT/PAC Incorporating a systematic way to ensure MDS data accuracy for all Medicare and Medicaid residents using predictive analytics can help to boost the credibility of a nursing facility and ultimately provide better care to its residents.

19 Why is Predictive Analytics Important? Delivering effective value-based healthcare requires identifying and mitigating risk by anticipating and preventing adverse events and outcomes. Predictive models have been a part of healthcare practice for decades. However, more advanced analytics have started to take shape to provide better visibility into characterizing a patient s current state and future risk. With the use of big data, it is possible to build models around predicting future events and outcomes, utilization, and overall risk.

20 Why is Predictive Analytics Important? These predictive models can be: incorporated into a clinical workflow to facilitate care management and identify individuals at risk used to perform risk adjustment on quality measures to account for patient severity employed to understand the treatment pathway with the greatest chance of success

21 Stakeholder Validation

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23 What Does All of This Mean?

24 Where does the Administrator Fit In? What are the CRITICAL executive considerations for applying Analytics Strategically?

25 Questions and Issues That Arose 25

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27 To Be Successful ANALYTICS MUST BE EMBEDDED AS AN ONGOING BUSINESS PROCESS INTO THE CULTURE OF YOUR ORGANIZATION. THIS CAN TURN YOUR FACILITY INTO AN AGILE, LEARNING ENTITY

28 So, What is the Future of Analytics Look Like?. We must continually look that the 3 V s a) Variety, Volume and Velocity of the Data we need to interpret to make the best management decisions possible.the future will contains multiple and parallel platforms.the future will have machines 28 that learn intuitively

29 So, What is the Future of Analytics Look Like? 4.The future will be self-serving for administrators 5.The future will be internally and externally collaborative 6.The future will be LIVE. 29

30 Does This Look Familiar? 30

31 Or This? 31

32 The 4-Phase Analytics Model 32

33 What we learned from Pilot Program in Miami Lewin proposed that human behavior should be seen as part of a continuum, with individual variations from the norm being a function of tensions between perceptions of the self and of the environment.

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38 Data Analytics in Health Care 38

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