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Getting Started With a Healthcare Predictive Analytics Program

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Getting Started With a Healthcare Predictive Analytics Program

These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.

These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.

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Getting Started With a Healthcare Predictive Analytics Program

  1. 1. GETTING STARTED WITH PREDICTIVE ANALYTICS Professor Bryan Bennett Northwestern University July 14, 2014
  2. 2. www.dataenabledhealth.com @enabledhealth Agenda • Predictive Analytics Primer • Predictive Analytics Program • The Analytics Program Lifecycle • Areas Where Predictive Analytics Can be Utilized Right Away • Major Challenges to Implementing a Healthcare Predictive Analytics Program
  3. 3. Predictive Analytics Primer
  4. 4. www.dataenabledhealth.com @enabledhealth What is Predictive Analytics? • Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. • It does not tell you what will happen in the future. – It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment. 4
  5. 5. www.dataenabledhealth.com @enabledhealth Gartner Even Goes Further • In addition to predicting what might happen, they add: – Analysis measured in hours or days (real-time or near real-time). – The emphasis on the business relevance of the resulting insights, like understanding the relationship between x and y. – An emphasis on ease of use, thus making the tools accessible to business users. Source: www.gartner.com 5
  6. 6. www.dataenabledhealth.com @enabledhealth Gartner Analytic Ascendancy Model 6
  7. 7. www.dataenabledhealth.com @enabledhealth Gartner Analytic Model Examples Type of Analytics Question Answered General Business Example Healthcare Example Descriptive Analytics What Happened? How many cars did we sell last year? How many patients were diagnosed with HBP last year? 7
  8. 8. www.dataenabledhealth.com @enabledhealth Gartner Analytic Model Examples Type of Analytics Question Answered General Business Example Healthcare Example Descriptive Analytics What Happened? How many cars did we sell last year? How many patients were diagnosed with HBP last year? Diagnostic Analytics Why Did It Happen? Why did we only sell x cars last year? Why did these patients develop HBP? 8
  9. 9. www.dataenabledhealth.com @enabledhealth Gartner Analytic Model Examples Type of Analytics Question Answered General Business Example Healthcare Example Descriptive Analytics What Happened? How many cars did we sell last year? How many patients were diagnosed with HBP last year? Diagnostic Analytics Why Did It Happen? Why did we only sell x cars last year? Why did these patients develop HBP? Predictive Analytics What Will Happen? If I run x advertising programs, how many cars can we sell? What are the chances Mr. Jones’ HBP will result in a stroke? 9
  10. 10. www.dataenabledhealth.com @enabledhealth Gartner Analytic Model Examples Type of Analytics Question Answered General Business Example Healthcare Example Descriptive Analytics What Happened? How many cars did we sell last year? How many patients were diagnosed with HBP last year? Diagnostic Analytics Why Did It Happen? Why did we only sell x cars last year? Why did these patients develop HBP? Predictive Analytics What Will Happen? If I run x advertising programs, how many cars can we sell? What are the chances Mr. Jones’ HBP will result in a stroke? Prescriptive Analytics How Can We Make it Happen? What do we need to do to sell x number of cars? Mr. Jones should be put on x medication to prevent his HBP from resulting in a stroke. 10
  11. 11. Getting Started With a Healthcare Predictive Analytics Program
  12. 12. www.dataenabledhealth.com @enabledhealth Predictive Analytics Implementation • Needs executive support • Needs a well-defined business challenge or query – Is there a relationship between certain variables and a health outcome? • Needs lots of data – Past and current • Need the right team – Quantitative (numbers) and qualitative (strategic) – Transform data from information to intelligence and insight for organization • Needs to be an integral part of the organization’s operations • Need to track results and update models 12
  13. 13. www.dataenabledhealth.com @enabledhealth Importance of Executive Support • Success is not all about the tools utilized or best analyst – Management support for analytics throughout the organization has proven to be a critical success factor, including: • Top down mandates for analytics, sponsors and champions • Being open to change and new ideas • Having unified analytics-driven focus on the patient’s health • Identifying and addressing operational threats to patient care
  14. 14. www.dataenabledhealth.com @enabledhealth Importance of Executive Support • Enterprise-wide solutions needs enterprise-wide support – Cross departmental silos • Need sufficient resources – Right people on the team – Commitment from other departments • Most important of all attributes – Makes the others happen
  15. 15. www.dataenabledhealth.com @enabledhealth Executive Support Leadership Management Leadership
  16. 16. www.dataenabledhealth.com @enabledhealth Executive Leadership Involves • Using a good solution selection process • Making sure you have the needed resources to complete the project – Financial and personnel • Having a vision for where you’re going • Building credibility with your team members • Raising your team members to their potential
  17. 17. www.dataenabledhealth.com @enabledhealth Wall of Shame • Examples are Everywhere • CEOs and CIOs are resigning or terminating due to problemed EHR implementations • Blame game – I.T. can’t force EHR or analytics program on staff • Like telling them how to practice medicine
  18. 18. www.dataenabledhealth.com @enabledhealth Implementation Complexity • Many EHR implementation mistakes • An analytics program implementation is much more complex than an EHR – On a scale of 1 to 10 • EHR = 5 Analytics = 12
  19. 19. www.dataenabledhealth.com @enabledhealth Importance of Executive Support • Healthcare Challenge – Executives have to manage organization’s staff to get their cooperation and buy-in • Particular challenge working with providers who might believe someone is trying to tell them how to practice medicine • Possible Solution – Be involved! – Get staff involved as early as possible – Focus on the benefits the analytics will offer
  20. 20. www.dataenabledhealth.com @enabledhealth Well-Defined Business Problem • How do we reduce readmissions? • How can we predict when someone might develop a more serious condition while in the hospital, i.e., stroke, heart failure, etc.? • The business problem must be measureable and the operation repeatable over a specific time period
  21. 21. www.dataenabledhealth.com @enabledhealth Well-Defined Business Problem • Measureable – The results of the analysis must be able to be measured or counted to determine if the prediction was accurate – For example: • Number of patients developing a certain condition
  22. 22. www.dataenabledhealth.com @enabledhealth Well-Defined Business Problem • Repeatable operation: – The attribute you choose to measure must occur regularly and have a repeatable pattern – For Example: • Patients unfortunately get readmitted regularly or develop other measurable conditions
  23. 23. www.dataenabledhealth.com @enabledhealth Well-Defined Business Problem • Specific time period – The variable being measured must have a specific beginning and ending – For Example: • Readmissions within 30, 60 or 90 days • Condition developed during hospital stay
  24. 24. www.dataenabledhealth.com @enabledhealth Well-Defined Business Problem • Healthcare Challenge – Business challenges are everywhere. The real problem is prioritizing which one to address first • Possible Solution – Find the challenge(s) that have the most potential of showing quick results and improving care • Picking ‘low hanging fruit’ is always good • People lose interest in longer term projects if results aren’t delivered soon
  25. 25. www.dataenabledhealth.com @enabledhealth Data Needs • A variety of data is needed for an effective predictive analytics program – Transactional and descriptive • In many cases, the data is usually kept in multiple silos across the organization • A data warehouse is typically needed to efficiently access all this data
  26. 26. www.dataenabledhealth.com @enabledhealth Organization Sales & Marketing Executive Accounting & Finance Purchasing/ Production Customer Service Corporate Data Silos Typical Data Model
  27. 27. www.dataenabledhealth.com @enabledhealth The Problem With Data Silos • Data silos are a repository of data stored and used by a single or few departments in an organization • Usually does not exchange data with other groups or departments – Data may not be updated • Impacts data integrity • Executive sponsor is needed to “open” these silos
  28. 28. www.dataenabledhealth.com Sales & @enabledhealth Marketing Executive Accounting & Finance Purchasing/ Production Customer Service Corporate Strategic Data Warehouse Corporate Strategic Data Warehouse
  29. 29. www.dataenabledhealth.com @enabledhealth Types of Data Needed • Some of the types of data needed: – Clinical Data – Demographic Data – Insurance / Reimbursement Data – Operational Data – Cost Data
  30. 30. www.dataenabledhealth.com @enabledhealth Data Needs • Healthcare Challenge (1) – There’s lots of data but a lot of it is locked in departmental silos which ultimately makes all the data useless • Possible Solution – Determine what data is valuable and have executive open regular access to it
  31. 31. www.dataenabledhealth.com @enabledhealth Data Needs • Healthcare Challenge (2) – There are no predefined predictive variables, like a FICO score • Some basic variables like high blood pressure may predict other potential problems • Don’t know which variable(s) will be most predictive • Possible Solution – Need a good team of modelers, analysts and clinicians to make sense of the model results
  32. 32. www.dataenabledhealth.com @enabledhealth Data Warehouse Challenges • A well designed data warehouse will provide the data services infrastructure to for an effective predictive analytics programs – One of the most overlooked aspects • Data warehouses also decrease the risk that current (or more recent) data gets accidentally overwritten with outdated (or less recent) data
  33. 33. www.dataenabledhealth.com @enabledhealth Data Warehouse Challenges • Healthcare Challenge – Healthcare data includes structured as well as unstructured (text) data • Possible Solution – Tools are currently available that take the unstructured data and convert it into some form of useful structured data
  34. 34. www.dataenabledhealth.com @enabledhealth The Analytics Project Team • Executive – to insure access to the needed data and the right people • Champion – ultimate owner of the analytics project whose problem or query we seek to answer. Could be department head or CMO • Project Manager – needed for larger projects to manage the day-to-day needs of the project and to make sure the analysts have the data and support they need
  35. 35. www.dataenabledhealth.com @enabledhealth The Analytics Project Team • Data Analyst – usually builds or gathers the data into a format or file the modeler will use • Modeler – usually a statistician who will actually build the models using various modeling tools • Clinician – needed to help the team make sense of the results / numbers from a healthcare point of view
  36. 36. www.dataenabledhealth.com @enabledhealth The Analytics Project Team • Healthcare Challenge – The challenge will be finding qualified people from an already scarce resource pool and getting them to accept the lower wage healthcare may pay • Possible Solution – The entire team must be paid market wages – Outsourcing might need to be an option • Bottom Line: GET HELP! – Especially when first starting
  37. 37. www.dataenabledhealth.com @enabledhealth Integral Part of the Organization • Organization must constantly be asking why, what, who, when, where and how • Not solved solely with technology – Must include processes to capture the right information – Personnel must be trained to capture and properly record information for analysis
  38. 38. www.dataenabledhealth.com @enabledhealth EHR Staffing Mistake • Many tried to implement EHRs with current staff – Have other responsibilities – Lacked skills set • Need specialized personnel to be successful
  39. 39. www.dataenabledhealth.com @enabledhealth Integral Part of the Organization • Healthcare Challenge – Everyone must buy-in to the results of the analytics program including clinical, finance and operational staff. • Possible Solution – Focus on the benefits of your analytics program – Show real results to providers • Avoid the impression that the analytics will tell them how to practice medicine
  40. 40. www.dataenabledhealth.com @enabledhealth Results Tracking and Model Updates • Need to determine if results were predicted by the model • Utilize prior results to improve on model – Update and test frequently • Otherwise, model may loose its effectiveness
  41. 41. www.dataenabledhealth.com @enabledhealth Results Tracking and Model Updates • Healthcare Challenge – With the right team in place this should not be an issue • Possible Solution – Have the right team but also the right process to manage and update the models – See Analytics Program Lifecycle
  42. 42. The Analytics Program Lifecycle
  43. 43. www.dataenabledhealth.com @enabledhealth Journey vs. Destination • A predictive analytics program implementation is a journey not a destination • The results and learnings from previous analysis and modeling should be incorporated in future analysis – Builds a stronger model • Must follow the Analytics Program Lifecycle
  44. 44. www.dataenabledhealth.com @enabledhealth The Analytics Program Lifecycle • Initial Research & Pre-Analysis – Defining the business problem or query • Data Gathering – Capturing data and creating file for analysis • Execution – Model building and applying to dataset • Post-Analysis – Did the model predict what was expected • Adjust model based on results – Use the results of the analysis to improve the model
  45. 45. www.dataenabledhealth.com @enabledhealth Pre Analysis Data Gathering Execution Adjustment Post Analysis Analytics Project Lifecycle
  46. 46. Areas Where Predictive Analytics Are Being Utilized Right Now
  47. 47. www.dataenabledhealth.com @enabledhealth Areas Showing Benefits Now • Improved Patient Flow • Disease Outbreak Prediction • Emergency Room Risks • Reduced Readmissions 47
  48. 48. www.dataenabledhealth.com @enabledhealth Improved Patient Flow • Can help an organization predict which resources will be needed at any given time • Predicting patient flow versus patient tracking • Reduces bottlenecks and wait times – Especially in the emergency room – Increases patient satisfaction 48
  49. 49. www.dataenabledhealth.com @enabledhealth Improving Patient Flow • Admissions and discharges – Efficient patient placement at admission – Find bottlenecks and drive for earlier or later discharge times • Capacity management – Identify underused beds and labs to better target patient usage – Improves patient care and increased revenues • Transport and housekeeping – Track job times and responsiveness to improve turnover 49
  50. 50. www.dataenabledhealth.com @enabledhealth Disease Outbreak Prediction • Google Flu Trends has been shown to foresee an increase in influenza cases 7 to 10 days earlier than the CDC – Based on online search trends • People with symptoms seek further information • Can pinpoint disease increase down to the hospital level – Resources can be allocated to prepare for influx of patients with the flu 50
  51. 51. www.dataenabledhealth.com @enabledhealth Predicting Disease Outbreak • Google Flu Trends found a close relationship between how many people search for flu-related topics and how many people actually have flu symptoms – A pattern emerges when all the flu-related search queries are added together • They compared query counts with traditional flu surveillance systems – Discovered that many search queries tend to be popular exactly when flu season is happening • By counting the frequency of the search queries they can estimate how much flu is circulating in different countries and regions around the world 51
  52. 52. www.dataenabledhealth.com @enabledhealth Emergency Room Uses • Used to predict whether a patient is likely to: – Go into cardiac arrest – Suffer a stroke – Potentially suffer from sepsis shock • While in the emergency room • Collecting real time data along with patient’s clinical history – Compare to prior patient data 52
  53. 53. www.dataenabledhealth.com @enabledhealth Reduced Readmissions • Risk of readmission in 30 days can be predicted in order to assist with the decision to release a patient • Reduces cost of readmission and the opportunity cost of a patient occupying a bed that could be used by someone else • Requires a proactive versus reactive approach 53
  54. 54. www.dataenabledhealth.com @enabledhealth Reducing Readmissions • The hospital must understand the factors effecting readmissions (discovery) – Create an algorithm built on data from past patients who were and were not readmitted, i.e. what was different? • Create automated processes to identify patients who are at risk for readmission based on clinical, demographics, etc. – Counter with a strategic response – Gaining information immediately from failures • Make sure personnel adher to the identified strategy – Evaluate effectiveness of their approach. 54
  55. 55. www.dataenabledhealth.com @enabledhealth Reducing Readmissions • Structuring a Project – Problem Definition – Data Needs – Modeling – Results Analysis
  56. 56. www.dataenabledhealth.com @enabledhealth Reducing Readmissions • Structuring a Project – Problem Definition – What patients are most likely to be readmitted – What are the causal drivers of readmission
  57. 57. www.dataenabledhealth.com @enabledhealth Reducing Readmissions • Structuring a Project – Data Needs – Patient vitals – Patient conditions – Departments involved in patient care – Specialties involved in patient care
  58. 58. www.dataenabledhealth.com @enabledhealth Reducing Readmissions • Structuring a Project – Modeling – Run simulations, optimizations and/or regression analysis to determine likelihood or readmission – Use all patients • Readmitted and Non-readmitted – Create an individual readmission risk score
  59. 59. www.dataenabledhealth.com @enabledhealth Reducing Readmissions • Structuring a Project – Results – Use risk score results to determine which patients likely to be readmitted in next 30 days • Real time results or daily • Hospital can exercise appropriate interventions – Communications – Post-discharge interventions
  60. 60. Major Challenges to Implementing a Healthcare Predictive Analytics Program
  61. 61. www.dataenabledhealth.com @enabledhealth Major Challenges Summary • Have involved executive support • Building a comprehensive data warehouse • Identifying predictive variables • Hiring analysts / modelers • Incorporating the program into the data collection and transformation process • Access to the analysis on devices at the point of patient contact
  62. 62. www.dataenabledhealth.com @enabledhealth Questions and Answers • Contact Information: – J. Bryan Bennett, “The Professor” • Healthcare Transformation Specialist, Data Scientist and Predictive Analytics Professor – E-mail • bryan@dataenabledhealth.com – Website / Blogs • www.dataenabledhealth.com • www.himssfuturecare/blog/1266 – Twitter • @enabledhealth 62

Editor's Notes

  • Based on Six Sigma and Project Management training
  • This is a graphical view of the lifecycle.

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