Creating a Data-Driven Healthcare Organization (HCO)

Transcription

1 Creating a Data-Driven Healthcare Organization (HCO) <#>

2 A preface to the following breakout session Disruptive Innovation: MPCA Annual Conference

3 Overview: Focus of session Discuss benefits, obstacles, and tips to create a data-driven healthcare organization (HCO) or health center Discuss systems and processes that increase organizational effectiveness Discuss big data analytics in HCO s 3

4 Intended Outcome Session participants will become intentional in pursuing process improvement to bring transformative results through informationbased decision making (or D3M) 4

5 Agenda 1 st Hour 1a. Introduction and overview 1b. Data Driven Decision Making (D3M) in Healthcare Organizations 1c. Creating a D3M Environment 1d. Designing Standardized and Simplified Systems and Processes for Repeatable Outcomes 5

6 Agenda 2nd Hour 2a. Big Data Applications in Healthcare: Clinical and Advanced Analytics 2b. Creating a D3M HCO A Case Study 2c. Conclusions 2d. Q&A 6

7 1a. Introduction and overview HCO s have been challenged to reduce the cost while improving the patient care The ways healthcare is obtained and delivered are being transformed by a number of realities Sources: IBM Software White Paper (Feb. 2013) Disruptive Innovation: MPCA Annual Conference

8 1b. Data Driven Decision Making (D3M) in HCO s Tips Because of these challenges and realities HCO s cannot afford using conventional approaches New data models must be developed to capture data effectively HCO s must go beyond collecting data for its own sake Disruptive Innovation: MPCA Annual Conference

9 1b. Data Driven Decision Making (D3M) in HCO s Tips (cont d) How data is defined makes a difference in our ability to sense problems Example: How no show is being coded? Effective HCO s typically focus on no more than 10 carefully identified goals HCO s should identify the most critical metrics Avoid Type III Errors 9

10 1c. How to Create a D3M Environment HCO s must create a culture of excellence and discipline Management by assumptions must be replaced by management by facts; Examples Systems and processes must be scrutinized if extraordinary outcomes are intended 10

11 1d. Standardizing and Simplifying Systems and Processes Predictable outcomes can ONLY be produced through system and process changes The creation of control plans helps ensure systematic implementation of the new system/process There is always room for improvement 11

12 1d. Standardizing and Simplifying Systems and Processes (cont d) Don t just solve the problems with quick fixes To be effective in making improvements, identify the constraints 12

13 1d. Standardizing and Simplifying Systems and Processes (cont d) Throughput and cycle time is just as important in healthcare Theory of Constraints can be applied to healthcare leading to CQI Identify the best process improvement practices in the industry 13

14 Break 10 minutes 14

15 2a. Big Data Applications in Healthcare Data is the new gold Data comes in both structured and unstructured form When data is used effectively, several things happen: Clinical analytics and advanced analytics can transform HCO s 15

16 2a. Big Data Applications in Healthcare (cont d) Both clinical analytics and advanced analytics are heavily data driven. IBM Software White Paper Data-driven healthcare organizations use big data analytics for big gains (Feb 2013) 16

17 2a. Big Data Applications in Healthcare (cont d) Conventional Approach HCO s have operated based on imprecise or incomplete cost and care measurements They did not have the comprehensive view of clinical and operational processes they needed to identify areas for improvement. Latest Trend The industry has begun to turn to data and analytics to improve service and reduce cost Sources: IBM Software White Paper (Feb. 2013) 17

18 2a. Big Data Applications in Healthcare (cont d) What is it all about? The ability to analyze a wide range of big data to determine what is happening right now with regard to Patient, staff, and population profiles Financial, clinical, and operational processes Big data comprises much larger volumes, wider varieties and greater velocities of data than most organizations have previously captured, stored, and analyzed Data comes from traditional sources and from nontraditional sources Sources: IBM Software White Paper (Feb. 2013) 18

19 2a. Big Data Applications in Healthcare (cont d) What are the potential benefits? It enables organizations to improve in many areas such as It supports more insightful analysis that can improve patient behavior Sources: IBM Software White Paper (Feb. 2013) 19

20 2a. Examples of areas yielding the greatest results Pinpointing which patients are the highest consumers of health resources or at the greatest risk for adverse outcomes, and then putting programs in place to optimize their health status and measure how they are doing against set targets Identifying treatments, programs and processes that are not delivering demonstrable benefits or are costing too much, and then determining how to replace them with more efficient and effective options Sources: IBM Software White Paper (Feb. 2013) 20

21 2a. Examples of areas yielding the greatest results (cont d) Managing population health by detecting vulnerabilities within patient populations during disease outbreaks or disasters, and then take preemptive action Building sustainability into a health system by bringing clinical, financial and operational data together to analyze resource utilization, productivity and throughput Sources: IBM Software White Paper (Feb. 2013) 21

22 2a. Examples of areas yielding the greatest results (cont d) Attracting the best and brightest clinicians, who can help to build and maintain an organization s reputation by offering innovative health IT systems and mobile technologies that enable collaboration and easy, secure remote access to patient records Sources: IBM Software White Paper (Feb. 2013) 22

23 2a. Clinical Analytics vs. Advanced Analytics Sources: IBM Software White Paper (Feb. 2013) 23

24 2a. The Analytics Continuum Sources: IBM Software White Paper (Feb. 2013) 24

25 2a. Big Data Applications in Healthcare Success Stories (cont d) Premier, the largest US healthcare alliance North York General Hospital Columbia University Medical Center The Rizzoli Orthopaedic Institute in Bologna, Italy The Hospital for Sick Children (SickKids), Toronto Sources: IBM Software White Paper (Feb. 2013) 25

26 2a. Big Data Applications in Healthcare (cont d) BUT we are not anywhere close to these gigantic organizations We can start small Perhaps health centers, under the umbrella of organizations like MPCA, can make some progress in that aspect Outsourcing possibility and using consultants/consulting firms 26

27 2a. Big Data Applications in Healthcare (cont d) Please check out the IBM Software White Paper for more information, especially on steps to become a datadrive HCO 27

28 2b. Creating a D3M HCO: A Case Study Fort Smith Health Center (FSHC) Improving the mammogram rate If you were the QI Director, what steps would you take to define the problem and develop counter measures for improving the mammogram rate? How would an evidence-based approach be helpful in solving the problem? 28

29 2b. Creating a D3M HCO: A Case Study In groups of 4-6 participants, review the case study, discuss the questions, and be prepared to report the findings to the bigger group Time allowed: 20 minutes 29

30 2c. Conclusions It is possible to create a D3M HCO; top management must be willing to enable planning, cultural and technological changes Data is a strategic asset that should be leveraged for CQI Operational improvement can translate into clinical and financial successes Potential benefits from D3M outweigh the cost 30

31 2c. Conclusions Big data/analytics can be applied to even small and medium sized FQHC s Standardizing and simplifying systems and processes can produce remarkable outcomes Cross-functional quality improvement initiatives can start on any processes within the organization The new challenges and realities calls for even more collaboration among HCO s 31

32 2d. Q&A 32

33 Thank you! 33

34 Sources 03.ibm.com/industries/ca/en/healthcare/d ocuments/data_driven_healthcare_organi zations_use_big_data_analytics_for_big_g ains.pdf what-is-theory-of-constraints-toc/ 34