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Population Health Management, Predictive Analytics, Big Data and Text Analytics

HCAD 6635 Health Information Analytics session 12
Population Health Management Analytics
Predictive Analytics
Big Data and its potential applications in Healthcare
Text Analytics
Public Health Analytics

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Population Health Management, Predictive Analytics, Big Data and Text Analytics

  1. 1. Population Health Management Analytics 3HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 1
  2. 2. Anatomy of Healthcare Delivery HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 2
  3. 3. 6 Population Health Management: Process and Business Requirements • Stages to achieving a Value-based Accountable Care Patient Panel Definition Targeted Populations & Outcomes Baseline Expenditures & Costs Accountability Models Financial Reconciliation Population Health Management • Identify Unique Patients • Assemble Records of Clinical Care • Define Bundles • Identify Unique Providers • Align Patients & Providers • Measure / Manage Care Delivery • Measure / Manage Care Relationships • Patient Panel Analytics • Defined Patients, Beneficiaries or Members • Segmentation • Outcomes: Clinical, Operational, Financial • Identify ACO Parties & Roles • Performance Targets & Metrics • Targeted Care Plans • EBM Guidelines for Required Care for Patient Needs • Historical Baselines • Align Patient with Provider Entity • Align Provider with ACO Entity • Calculate Service Fees & Savings Targets • Hierarchical Segmentation & Aggregation • Anticipated Services, Charges & Costs • Collaborative Care Delivery Models • Transitions in Care • Communications, Handoffs, Follow- ups • Contracts, Roles, Responsibilities • Shared Metrics, Benefits & Risks • Retrospective Payments • Shared Savings & Costs • Value Realization • Allocated Gains (Losses) • Billing & Payment Distribution • Compliance & Adherence Targets • Patient Stratification • Comparative Outcomes & Quality Metrics • Prospective & Bundled Payment Models • Predictive Risk Modeling • Performance Optimization • Market Share & Competitive Analytics HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 3
  4. 4. 7 Population Health Analytics: Analytic Needs Assessment How do we manage patient cohorts systematically? How do we focus and integrate our care delivery across populations & care settings? Population Health Management Do we understand our charges, payments and costs? Are we reconciling these with our care plans and our accountability models? Financial Reconciliation How do we implement & measure accountability? Where and by whom are value and costs introduced into our delivery processes? Accountability Models What are our baseline data on these targets, with this payer? How do these align with our contract terms across payer types? Baseline Expenditures & Costs What are our current targets? What quality / results are we seeing? Are they consistent? Where do we see under- or over-performance? Targeted Populations & Outcomes Who are patients? What treatments are they receiving? What other providers are they seeing? At what locations? With what frequency? Patient Panel Definition HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 4
  5. 5. 24 Population Health Management Analytics for Value-Based Healthcare PHC Analytics Clinical Strategic Planning IT Practice Mgmt Marketing Finance • Revenue Cycle • Costs, Margin • Payer Mix • Stratification • Outcomes • Quality & Safety • Growth • Market Share • Competition • Architecture • Data Quality • Tools, Applications • Security, Governance • Patient Satisfaction • Panel Management • Continuum of Care • Outreach • Physician Liaison • Relationship Mgmt • Service Improvement • Integrating Analytics for Clinical, Operational and Financial Improvement HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 5
  6. 6. The 8 Building Blocks of Successful Accountable HealthcarePayforReportingPayforOutcomes EHR/PMS/ E-Prescribing 2. Automating and Integrating Fragmented Stakeholders Information Exchange (HIE) 3. Sharing Clinical, Operations and Financial Information Aggregation & Analytics 4. Aggregating Siloed Data and Gaining Insight Decision Support 5. Transforming collected data into clinical knowledge Healthcare Portals and Medical Homes 6. Making clinical information accessible and “team-based” care possible Outcomes Measurement & Reporting 7. Establishing Core Measures and Reporting Outcomes Risk Sharing 8. Enabling Population Based Management and Risk Sharing Models Converged Medical Infrastructure 1. Establishing Standardized and Optimized IT Platforms HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 6
  7. 7. Population Health Management: Challenges in Culture, Stakeholders, Data, Technology and Governance • Data From Multiple Source Systems of Record and Points of Origin  Differing Formats and Semantics  Inconsistent Taxonomies  Differing Data Granularities • Technical Challenges  Timing and Granularity Differences and Conflicts  Access to data stored in the cloud  Positioning for Big Data Opportunities • End-User Experience  Consistent but Responsive (Variable, Tailorable) Experience  Power User vs. Ease of Use  Education on Source, Meaning and Veracity of Data Elements • Data Governance  Lack of consistent Enterprise-wide definitions  Different groups use similar terminology for different data and meanings HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 7
  8. 8. 11 HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 8 Population Health Management: Navigating Complex Data Spaces Patient Organization Provider Location Contracts Payer Claims Payments Encounter Charges Costs Diagnosis Treatments Chronic Condition Disease Group Procedures Medications Margin Events Data Flow Model …
  9. 9. 8 HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 9 Population Health Management: Data Integration Challenges • Demographics • History • Reported Outcomes • Location • Specialty • Relationships • Location • Care Team • Structure • Locations • Legal Entity • Contracts • Care Mgmt Teams • Inpatient • Outpatient • Pharmacy • Beneficiary History • Payers • Charges, Payments & Adjustment • Costs • Margin • Risk Contracts • Diagnosis • Chronic Conditions • Labs & Results • Procedures & Medications • Quality • Appts Scheduling • Utilization & Throughput • DRG • Location
  10. 10. 9 Population Health Management Enterprise Data Architecture DataIntegration&Transformation Dashboards & Analytic Views Contract Measures Performance Summary Baseline Expenditure Provider Profile DataAccess–Navigation&Security Reports Capture Integration and Transformation Consumption Extensible Data Architecture Provider Standard Data Models Patient Location Claim Reference Other Master Data Encounter Patient Panel Analytics Targeted Populations & Outcomes Baseline Expenditures & Costs Accountability Models Financial Reconciliation Population Health Management Health System EMR Billing MPI Provider Master Coding Payers Members Claims HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 10
  11. 11. 23 Population Healthcare Management Analytics Accountable Care and Population Management Platform Patient Panel Definition Targeted Populations & Outcomes Baseline Expenditures & Costs Accountability Models Financial Reconciliation Population Health Management Integrated Data Platform Changes to Processes & Operations Changing Healthcare Model Population & Practice Models HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 11
  12. 12. 22 Population Health Management: Mobilizing for Value-Based Care Workflows managing integrate data, care delivery, communications and metrics • Physician Office • Other Care Settings Labs Data Capture • Analytics • Patient Registry • Financial & Quality Measures Workflow Triggers, Alerts & Escalation • Patient Registration, Scheduling • Call Center • Patient Home • Web Access • Progress Review • Assessment & Stratification • Individualized Care Plan • Discharge EMRs Quality Performance • Outreach Workflow Management Patient Engagement Care PlansPractice Mgmt Cost Models HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 12 Integration & HIE Metrics
  13. 13. Predictive Analytics 3HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 13
  14. 14. • 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. • Gartner goes a step further:  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 HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 14 What is Predictive Analytics?
  15. 15. Predictive Analytics in Population Health Management • It must be timely • It must be role-specific • It must actionable Risk scores (stratification) What-if scenarios (simulation) Geo-spatial analysis (mapping) HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 15
  16. 16. • Risk stratification scoring can assist in • Prioritizing clinical workflow • Reducing system waste • Creating financially efficient population management. • Well-established risk stratification scores of low-risk, high-risk, and rising-risk can play a key role in several healthcare scenarios. Risk Stratification HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 16
  17. 17. Overview of Risk Stratification Methods • Hierarchical Condition Categories (HCCs) - CMS Medicare Advantage Program  Contains 70 condition categories selected from ICD codes and includes expected health expenditures. • Adjusted Clinical Groups (ACG) - Johns Hopkins University  Uses both inpatient and outpatient diagnoses to classify each patient into one of 93 ACG categories. It is used to predict hospital utilization. • Elder Risk Assessment (ERA)  Uses age, gender, marital status, number of hospital days over the prior two years, and selected comorbid medical illness to assign an index score to each patient (for adults 60 years and older). • Chronic Comorbidity Count (CCC) - Clinical Classification Software from AHRQ  Is the total sum of selected comorbid conditions grouped into six categories. • Minnesota Tiering (MN) – Major Extended Diagnostic Groups (MEDCs)  Groups patients into one of five tiers from Tier 0 (Low: 0 Conditions), Tier 1 (Basic: 1 to 3), Tier 2 (Intermediate: 4 to 6), Tier 3 (Extended: 7 to 9), to Tier 4 (Complex: 10+ Conditions). • Charlson Comorbidity Measure - The Charlson Model  Predicts the risk of one-year mortality for patients with a range of comorbid illnesses.  Uses administrative data.  Categorizes the presence/absence of 17 comorbidity definitions and assigns patients a score from one to 20, with 20 being the more complex patients with multiple comorbid conditions.  Used for predicting future poor outcomes. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 17
  18. 18. Stratifying Population and Predicting Risks Based on Comorbidity • A Histogram (Frequency Distribution) of a Charlson Index Score for a population of heart failure patients. • The Frequency Distribution can help PHM to gauge level of risk of the population. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 18 • Charlson/Deyo Model based on multiple inputs:  # and types of comorbidity  Age  An index score of risk assigned to each patient  Used as a general risk stratifier  Used as a mortality predictor  Can be incorporated in many applications as a filter to identify high risk patients
  19. 19. Creating a Predictive Model of Heart Failure Readmission Risks HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 19 • Multiple clinical and demographic parameters based on historical data  Comorbidity Index  Others include race, gender, family history, vital signs, length of stay (LOS), propensity and predisposition of patients to diseases, etc.  Regression on historical data ran to the weight that should be assigned to each parameter in the model.  Those weights determine the impact each parameter has on the predicting readmission • Applying the predictive model to flag high-risk patients and suggest pro- active actions needed
  20. 20. Stratifying Population and Predicting Risks Based on Comorbidity HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 20
  21. 21. Simulation/What-if Scenarios • Calculating the amount of opportunity dollars to capture in healthcare financial administration to reduce variation in a specific clinical care process. • Provides clinicians and administrators a safe glimpse into “what if” simulation and assess the likely outcomes of a given combination of events in healthcare delivery . • Optimizing campaign budget allocation in healthcare marketing. • Allows payers to define premiums effectively in healthcare insurance exchange markets HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 21
  22. 22. 22 Areas Showing Benefits Now • Improved Patient Flow • Reduced Readmissions • Disease Outbreak Prediction • Emergency Room Risks HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  23. 23. 23 • Enables prediction 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 HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang Improve Patient Flow • Improves admissions and discharges workflows  Result in efficient patient placement at admission  Find bottlenecks and drive for earlier or later discharge times • Manages capacity needs  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
  24. 24. 24 • Predict the risk of readmission in 30 days to a patient to assist with the decision to release a patient • Reduce cost of readmission and the opportunity cost of a patient occupying a bed that could be used by others • Requires a proactive versus reactive approach to be effective HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang Reduced Readmissions • Identify the factors effecting readmissions  Discover and use descriptive analytics based on prior readmission data  Create an algorithm to predict who is likely readmitted • Create automated processes to identify patients who are at risk for readmission based on clinical, demographics, etc.  Counter with a strategic response  Gain information immediately from failures • Make sure personnel adhere to the identified strategy  Periodically valuate effectiveness
  25. 25. Disease Surveillance Monitoring and Reacting to Outbreaks like Ebola Monitoring chief complaint /reason for admission data in Admit, Discharge, and Transfer (ADT) data streams. Monitoring coded data collected in Electronic Health Records (EHRs). Monitoring billing data. • Diseases also have a data profile (symptoms, perhaps discrete lab results or other diagnostics like imaging). • Boolean logical determinations, based on complete and valid data, may point to opportunities for computer-assisted treatment decision- making. Data Profile Alert HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 25
  26. 26. Disease Surveillance in the Near-Term • Monitoring ADT messages: Use the chief complaint/reason for admission data in an ADT message. Advantage: Real-time, upon presentation of the patient at a healthcare facility. Disadvantage: Lacks codified, computable data in the data stream, requiring natural language processing (NPL). • Analyzing Coded EHR and Other Clinical Data: Monitors coded data (SNOMED or ICD) for diagnosis, labs tests and results, and diagnostic imaging. Advantage: The most precise method; unlikely to ever be a real-time option due to the inherent nature of healthcare delivery. Disadvantage: Timeliness of treatment data will lag the decision making process too late for effective decision making. • Analyzing Coded Data From Billing Systems: This has all the problems of the other two. It’s not unusual for revenue cycle processes and systems to take over 30 days to drop a bill. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 26
  27. 27. • Google Flu Trends shown to foresee an increase in influenza cases 7 to 10 days earlier than the CDC  Descriptively analyzed online search trends  Discovered that many search queries tend to be popular exactly when flu season is happening  Hypothesized people with flu symptoms seek information  Compare query counts with traditional flu surveillance systems  Identify correlation between how many people search for flu-related topics and how many people actually have flu symptoms  Aggregate all flu-related search queries to establish a pattern  Estimate how much flu is circulating in different countries and regions (Geo-special analytics)  Can even pinpoint disease increase down to the hospital level  Resources can be allocated to prepare for influx of patients  Will discuss how to supplement this with text analytics HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 27 Disease Outbreak Prediction using Social Media Analytics
  28. 28. 28 • Used to predict whether a patient is likely to:  Go into cardiac arrest  Suffer a stroke  Potentially suffer from sepsis shock • Collecting real time data (real-time operational analytics) along with patient’s clinical history  Compare to prior patient data HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang Emergency Room Use
  29. 29. Source: HealthIT Analytics, "Just 15% of Hospitals Use Predictive Analytics Infrastructure," Jennifer Bresnick, March 24, 2015 http://healthitanalytics.com/news/just-15-of-hospitals-use-predictive-analytics-infrastructure/ • Just 15% of hospitals are using advanced predictive analytics to stay one step ahead of preventable hospital readmissions, and hospital-acquired conditions. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 29 • Data-Driven: letting the data dictate how we treat and manage customers with as much automated decisions as possible • Data-Enabled: providers are provided real- time or near real-time information to enable them to make better decisions and diagnoses based on hundreds or thousands of patients with similar symptoms and demographics Predictive Analytics is Key to Transform an Enterprise’s Culture from Data-Driven to Data-Enabled
  30. 30. Data-Enabled Healthcare Organization Change Model: People, Organization, Culture, Process, Data and Technology) Paper-Based Healthcare Organization Data-Enabled Healthcare Organization Resistance To Change Isolated Acceptance Phase 3 Phase 4 DATA / TECHNOLOGY ORGANIZATIONAL / PEOPLE PROCESS / WORKFLOWSMinimal Data Capture Network-Wide And Outside Data Capture Phase 3 Phase 4 Phase 4 EHR Implementation Analysis & Modeling Integration of Data Sources Predictive 30PrescriptiveHCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  31. 31. Advanced Analytics 12HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 31
  32. 32. Big Data and Analytics Big data “describes large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.”1 Big Data represents big opportunity U.S. health care data alone reached 150 exabytes in 2011. Big data for U.S. health care will soon reach zettabyte (1021 gigabytes) scale and even yottabytes (1024 gigabytes) not long after. 32 1. Hartzband, D. D. (2011). Using Ultra-Large Data Sets in Health Care. 2011 Sessions (p. 3). e-healthpolicy.org. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  33. 33. Where Does Big Data Come From? Web and social media data: Clickstream and interaction data from social media such as Facebook, Twitter, Linkedin, and blogs. Machine-to-machine data: Readings from sensors, meters, and other devices. Transaction data: Health care claims and other billing records. Biometric data: Fingerprints, genetics, handwriting, blood pressure, medical images, retinal scans, and similar types of data. Human-generated data: Unstructured and semi-structured data such as electronic medical records (EMRs), physicians’ notes, email, and paper documents. 33 SOURCE: Institute for Health Technology Transformation. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  34. 34. A Changing Healthcare Data Environment 34HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang • Healthcare is entering into a phase of ‘post EMR’ deployment where HCOs are “keen on gaining insights and instituting organizational change from the vast amounts of data being collected from their EMR systems”. • HCOs must reduce costs and improve quality of care by “applying advanced analytics to both internally and externally generated data”. • Larger volumes of structured and unstructured data can now be managed and analyzed through “faster, more efficient and cheaper computing (processors, storage, and advanced software) and through pervasive computing (telecomputing, mobile devices and sensors)”. SOURCE: HIMSS, “What Is Big Data?” http://www.himss.org/ResourceLibrary/genResourceFAQ.aspx?ItemNumber=30730
  35. 35. What Makes Big Data Big? HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang Factor Name Symbol # Bytes 103 kilobyte KB 1,024 106 megabyte MB 1,048,576 109 gigabyte GB 1,073,741,824 1012 terabyte TB 1,099,511,627,776 1015 petabyte PB 1,125,899,906,842,624 1018 exabyte EB 1,152,921,504,606,846,976 1021 zettabyte ZB 1,180,591,620,717,411,303,424 1024 yottabyte YB 1,208,925,819,614,629,174,706,176 35
  36. 36. The Four Dimensions of Healthcare Data Adapted from: Sun J. & Reddy CK. Big DataAnalytics for Healthcare. http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare Big Data 36HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang Volume Veracity VarietyVelocity
  37. 37. The Four Dimensions of Healthcare Data 37HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang • Volume – The amount of data being generated and stored. Typical “big data” datasets can range from terabytes (1012 bytes) to petabytes (1015 bytes) and exabytes (1018 bytes).  Traditional database technologies (such as Relational Database Management Systems, or RDBMS) and query tools (such as SQL) are unable to scale efficiently to such volumes, necessitating new approaches to data storage, management, and analysis. • Variety – The number of different data sources has grown, ranging from more traditional EMR data to website clickstream data and data from social media sites (i.e., Twitter). • Velocity – Refers to the speed at which data is generated (through its numerous sources), accumulated (in associated storage systems), and must be processed. • Veracity – Not part of the original Gartner “big data” definition, but is an indication of data quality (i.e., accuracy and completeness), trust (credibility of the source), uncertainty, and suitability (of data for target audience).
  38. 38. Big Data Classification • http://www.ibm.com/developerworks/library/bd-archpatterns1 39HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  39. 39. Big Data in Healthcare Today: Reality Check • Volume: EMRs collect huge amounts of data, but only half of the tables in an EMR database (400 to 600 tables out of 1000s) are relevant to the current practice of medicine and its corresponding analytics use cases. • Variety: Yes, but most systems collect very similar data objects with an occasional tweak to the model. • Most health systems meet majority of analytics and reporting needs today without big data. • Not close to stretch the limits of what analytics can accomplish with traditional relational databases. • Most healthcare institutions swamped with some very pedestrian problems such as regulatory reporting and operational dashboards. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 41
  40. 40. However, Big Data Is Coming To Healthcare • New use cases (e.g. wearable medical devices and sensors) drive the need for big-data-style solutions. • Embark on the journey of analyzing text-based notes (chief complaints, clinical charts). • Big data indexing techniques add real value to healthcare analytics. • The introduction of genomic data and precision medicine practices. • Genomic sequences are huge files and the analysis of genomes generates even more data. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 42
  41. 41. Big Data: the Internet of Things, Care Management and Clinical Trial • SAS describes the IoT as: a growing network of everyday objects from industrial machines to consumer goods that can share information and complete tasks while you are busy with other activities, like work, sleep, or exercise. Volume, Variety, Velocity and Veracity: • Wearable fitness devices collect personal health data (heart rate, weight, trending) and sends that data into the cloud will be part of this IoT. • Accountable care organizations and Population Health Management practices want to keep people at home and out of the hospital will deploy sensors and wearables. • Healthcare institutions and care managers, using sophisticated tools, will monitor this massive data stream and the IoT to keep their patients healthy. • Clinical trials of pharmaceuticals and medical devices will deploy sensors to report outcomes. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 43
  42. 42. Big Data: Predictive Analytics, Social economic Analytics and Geo- spatial Analytics • Real-time alerting. • Predictive analytics used to dissect socioeconomic data. • Geo-spatial analytics used to dissect geographic data  Mapping layers and predictive analytics are routinely used to forecast weather, optimize supply chains, and support military deployment. • Data show people in a certain zip code are unlikely to have a car. A patient from that zip code discharged from the hospital might have difficulty going to a follow-up appointment at a distant physician’s office • Used to predict missed appointments and noncompliance with medications.  Visual and effective approach to decision-making. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 44
  43. 43. Big Data: Predictive Analytics and Prescriptive Analytics • Leverage historical data from other patients with similar conditions, predictive analytics can predict the trajectory of a patient over time.  Based on predictive algorithms  Using programming languages such as R and big data machine learning libraries • Once we can accurately predict patient trajectories, we can shift to the Holy Grail– Prescriptive Analytics. • Intervene to interrupt the patient’s trajectory and set the proper course. • Big data is well suited for these futuristic use cases. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 45
  44. 44. The Human Genome Project and Genomic Medicine • The Human Genome Project, completed in April 2003, made reading the full genetic blueprint for human beings a reality. • In the future clinicians will be able to practice genomic medicine and personalized care. • It also has profound implications for the future of analytics. • The cost of sequencing a human genome has fallen from about $ 1 billion in 2001 to less than $1,000 today. • As costs drop, advances in genomic research are accelerating. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 46
  45. 45. Using Genomics to Diagnose and Treat Disease • A study published in the February 2014 edition of the NEJM demonstrates that analyzing fetal DNA in a pregnant woman’s blood was a more accurate and less invasive way of screening for Down syndrome and other chromosomal disorders than methods such as ultrasound imaging and blood tests. • Discovery of the genomic defects for more than 5,000 inherited diseases. • Early genomic medicine success lies in rare inherited diseases. These diseases afflict more than 25 million Americans. • Many molecular diagnostic kits and orphan drugs approved by FDA. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 47
  46. 46. Using Genomics to Diagnose and Treat Disease • The analysis of genomes is guiding treatment for various types of cancer. • Many cancer types can be categorized by genomic traits and divided into subtypes. Treatment are being developed based on the underlying genetic signature. • This approach offers patients the most efficacious treatment with minimal side effects. • Genomics is starting to be used to improve the efficacy of medications and how clinical care is delivered in oncology, immunology and other specialized medical practices. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 48
  47. 47. Personalized Medicine through Genomics and Sensor Devices • In the future, physicians will tailor treatment for many diseases based on an individual patient’s genomic profile. • Genes, RNAs and proteins are also impacted by our lifestyle, habits, and environment. • Wearable devices will be able to record physiologic data such as temperature, heart rate, blood pressure, blood oxygenation, heart rhythm, sleep patterns, and weight. • Correlational and causal analysis can be performed. • Personalized medicine promises to yield more effective diagnostic measures and treatments leading to healthier, longer lives and lower healthcare costs. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 49
  48. 48. Big Data: Genomics and Personalized Medicine • The amount of data produced by personalized medicine is propelling healthcare into the realm of big data. Genomic-based medicine offers tremendous promise and power to revolutionize clinical care, and it will exponentially change healthcare analytics. • Genomics produces huge volumes of data. Each human genome is comprised of over 3 billion base pairs (1K gigabytes of data). • Sequencing human genomes quickly adds up to hundreds of petabytes of data; and the data created by-omics (bioinformatics, proteomics, metabonomics …) multiplies many times. • Research and translational medicine created an analytics discipline called bioinformatics which developed many tools and databases used by healthcare predictive analytics. HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 50
  49. 49. HIMSS Big Data Architecture HIMSS, “A Big Data ReferenceArchitecture” http://www.himss.org/ResourceLibrary/genResourceFAQ.aspx?ItemNumber=30736 53HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  50. 50. Conceptual Big Data Analytics Architecture http://hortonworks.com/blog/modern-healthcare-architectures-built-with-hadoop/ 54HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  51. 51. Big Data Capabilities McKinsey – The “Big Data” Revolution in Healthcare http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care 55HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  52. 52. Big Data Implementation Success Factors 56HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang • Test and learn: An agile approach with rapid releases enables organizations to fine-tune their projects while they’re in progress. Traditional legacy systems were better suited to a “waterfall” approach, where technology was introduced all at once. Big Data projects should focus on specific business goals and allow cross-pollination of ideas to better understand what’s possible, making a “rapid release” approach much better. • Incremental adoption: Build a center of competency and cross-pollinate expertise among business experts, data scientists, and data engineers. This approach enables business units to leverage a common talent pool and a shared approach, eliminating the risk of data silos, providing for common governance, and avoiding redundant storage and processing by different departments. • Change management: Think about key stakeholders for the initiative, understand their concerns, get their buy in and invest in early pilot systems that demonstrate the value that can be generated through a Big Data investment. http://thinkbiganalytics.com/resources/big-data-whitepaper/right-start-big-data-projects
  53. 53. Working With Unstructured Data: Text Analytics HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 57
  54. 54. Unstructured Data 58HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang • Unstructured data has no identifiable structure. • Typically includes image/objects data (i.e., image, video, & sound files), text and other data types that are not codified and easily analyzed using conventional database tools. • In addition to EMR and other clinical/operational data systems, consider other sources of untapped knowledge in your healthcare organization: – Emails – Miscellaneous documents (policies, procedures, guidelines) Source: http://searchstorage.techtarget.com/feature/What-is-unstructured-data-and-how-is-it-different-from-structured-data-in-the-enterprise
  55. 55. Structured and unstructured information <ICD9> <Code>413.9</Code> <Descr>NOS angina pectoris</Descr> </ICD9> HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 59
  56. 56. Structured versus Unstructured Data • “The trick is to create value by extracting the right information from both internal and external data sources. That is what the science of data and art of business analytics needs to learn to extract from larger and larger sets of unstructured data.” http://www.datasciencecentral.com/profiles/blogs/structured-vs-unstructured- data-the-rise-of-data-anarchy 60HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  57. 57. In Healthcare, 80% Of Data Untapped for KPIs and Analytics • Needle Stick Injury Rate • Reintubation Rate • Ventilator Associated Pneumonia (VAP) • Blood Stream Infection Due to Central Line • Urinary Catheter Related Infection • Overall Employee Satisfaction • Patient Satisfaction • Standardized mortality rate (SMR) • Iatrogenic Pneumothorax • Decubitus ulcer • Length of Stay • ICU readmission rate • Patients' Fall Rate • Medication error • Adverse Events/Error Rate Key Performance Indicators (KPIs) Transaction Records Qualitative Human Data Quantitative Machine Data Admission notes Discharge summaries Progress notes Imaging study results Consultant reports Financial & Operational Transactions Medication records Laboratory results Physiologic testing Biometric sensors RFID tags Partial Intelligence HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 61
  58. 58. The Nuances of Clinical Documentation: Institutional Memory • Context  Heart attack mentioned in History of Present Illness  Heart attack mentioned in Family History • Meaning (in ED triage notes)  DOB to indicate Date of Birth  DOB to indicate Difficulty of Breathing • Negation  The patient denied shortness of breath or chest pain HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 62
  59. 59. Approaches To Unstructured Information Text processing technologies: keyword, NLP, probabilistic Taxonomies, ontologies: • SNOMED • UMLS, • ICD9/10 • RxNorm, • LOINC Clinical documentation: • Encounter / admission / progress / procedure notes, • Discharge summaries, • Test results Formal published medical knowledge: • literature (PubMed), • protocols, • guidelines Unconventional sources: • Social media, • call center transcripts • Internet of Things • Social Media HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 63
  60. 60. Healthcare Text Analytics Use Cases • Clinical chart review is necessary for:  Compliance Reporting:  Clinical Quality Measures (CQM’s),  Core Measures Quarterly,  PQRS,  eRx reporting,  Hospital Inpatient Quality Reporting Program,  CHIPRA, (Children’s Health Insurance Program Reauthorization Act),  ACO Programs  Public Health Data:  Syndromic surveillance,  CDC,  Cancer registries,  Immunization reports,  Adverse drug events  Research and Population Health Management:  Cohort identification,  Recruitment of eligible subjects • Manual chart review is labor intensive, time consuming, and has human variation HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 64
  61. 61. Text Analytics: Understanding Hospital Acquired Conditions • HAC’s include adverse drug events, hospital acquired infections, procedural complications and incidents related to falls  The incidences per 1,000 visits are:  65 incidents due to adverse drug events  60 events due to hospital acquired infections  51 events due to procedural complications  15 incidents related to falls • HAI’s result in extra costs of $5-10M /year for average hospital • ADE’s cost the average hospital about $5.6M/year • ADE’s increase LOS by 4.6 days and increases costs by at least $5K/incident Tinoco A, Evans R, et al. J Am Med Inform Assoc 2011;18:491-497 "Computerized surveillance systems that can access both coded and free text data such as that found in unstructured narratives may improve surveillance without requiring the time and cost associated with manual chart review, alone.“ HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 65
  62. 62. Texas Analytics: Looking for Depression and Bipolar Syndromes • 1 in 10 adults met criteria for current depression, 4.1% met the criteria for major depression 1 • Median delay from onset of depression to the beginning of treatment was estimated to be eight years 2 • Treatment prevalence rates of comorbid depression for some chronic conditions is significantly lower than the expected comorbidity rates (e.g., 16% treated vs. 45% expected) 3 • Comorbid depression increases medical services costs by average of $505/member/mo. and has significantly elevated odds ratios with poorer self care (e.g., high fat intake > 6x/wk, smoking, exercise < 1x/wk) 3 1: MMWR 2010;59(38);1229-1235 2: Melek, S. Halford, M. Measuring the Cost of Undiagnosed Depression. Contingencies. Jul/Aug 2012. Pps. 64-70 3: Melek, S. Norris, D. Chronic conditions and comorbid physchological disorders. Milliman Research. 2008 HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 66
  63. 63. Texas Analytics Applications in Healthcare Reconciliation ID discrepancies between diagnostic code and clinical notes Monitor KPIs and Metrics Reporting Abstraction Rapid Chart Access HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 67
  64. 64. Natural Language Processing (NLP) for Hawaii Syndromic Surveillance NLP Classifier Triage clerk Chief Complaint Cough/Fever Syndromic Category • Respiratory • GI • Neurological • Rash • Hemorrhagic • Botulinic • Constitutional Alarms Epidemiology Team Chief Complaint HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 68
  65. 65. Managing Structured & Unstructured Data 69HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
  66. 66. Healthcare Analytics Maturity Model HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 70
  67. 67. Level 8 Cost per Unit of Health Reimbursement & Prescriptive Analytics: Providers Analytic motive expands to wellness management and mass customization of care. Physicians, hospitals, employers, payers and members/patients collaborate to share risk and reward (e.g., financial reward to patients for healthy behavior). Analytics expands to include NLP of text, prescriptive analytics, and interventional decision support. Prescriptive analytics are available at the point of care to improve patient specific outcomes based upon population outcomes. Data content expands to include genomic and familial information. The EDW is updated within a few minutes of changes in the source systems. Level 7 Cost per Capita Reimbursement & Predictive Analytics: Analytic motive expands to address diagnosis-based, fixed-fee per capita reimbursement models. Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling, forecasting, and risk stratification to support outreach, triage, escalation and referrals. Patients are flagged in registries who are unable or will not participate in care protocols. Data content expands to include external pharmacy data and protocol-specific patient reported outcomes. On average, the EDW is updated within one hour or less of source system changes. Level 6 Cost per Case Reimbursement & The Triple Aim: The “accountable care organization” shares in the financial risk and reward that is tied to clinical outcomes. At least 50% of acute care cases are managed under bundled payments. Analytics are available at the point of care to support the Triple Aim of maximizing the quality of individual patient care, population management, and the economics of care. Data content expands to include bedside devices and detailed activity based costing. Data governance plays a major role in the accuracy of metrics supporting quality-based compensation plans for clinicians and executives. On average, the EDW is updated within one day of source system changes. The EDW reports organizationally to a C-level executive who is accountable for balancing cost of care and quality of care. Level 5 Clinical Effectiveness & Population Management: Analytic motive is focused on measuring clinical effectiveness that maximizes quality and minimizes waste and variability. Data governance expands to support care management teams that are focused on improving the health of patient populations. Permanent multidisciplinary teams are in-place that continuously monitor opportunities to improve quality, and reduce risk and cost, across acute care processes, chronic diseases, patient safety scenarios, and internal workflows. Precision of registries is improved by including data from lab, pharmacy, and clinical observations in the definition of the patient cohorts. EDW content is organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries. Data content expands to include insurance claims. On average, the EDW is updated within one week of source system changes. Level 4 Automated External Reporting: Analytic motive is focused on consistent, efficient production of reports required for regulatory and accreditation requirements (e.g. CMS, Joint Commission, tumor registry, communicable diseases); payer incentives (e.g. MU, PQRS, VBP, readmission reduction); and specialty society databases (e.g. STS,NRMI, Vermont-Oxford). Adherence to industry-standard vocabularies is required. Clinical text data content is available for simple key word searches. Centralized data governance exists for review and approval of externally released data. Level 3 Automated Internal Reporting: Analytic motive is focused on consistent, efficient production of reports supporting basic management and operation of the healthcare organization. Key performance indicators are easily accessible from the executive level to the front-line manager. Corporate and business unit data analysts meet regularly to collaborate and steer the EDW. Data governance expands to raise the data literacy of the organization and develop a data acquisition strategy for Levels 4 and above. Level 2 Standardized Vocabulary & Patient Registries: Master vocabulary and reference data identified and standardized across disparate source system content in the data warehouse. Naming, definition, and data types are consistent with local standards. Patient registries are defined solely on ICD billing data. Data governance forms around the definition and evolution of patient registries and master data management. Level 1 Integrated, Enterprise Data Warehouse: At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3 data, Revenue Cycle, Financial, Costing, Supply Chain, and Patient Experience. Searchable metadata repository is available across the enterprise. Data content includes insurance claims, if possible. Data warehouse is updated within one month of changes in the source system. Data governance is forming around the data quality of source systems. The EDW reports organizationally to the CIO. Level 0 Fragmented Point Solutions: Vendor-based and internally developed applications are used to address specific analytic needs as they arise. The fragmented Point Solutions are neither co-located in a data warehouse nor otherwise architecturally integrated with one another. Overlapping data content leads to multiple versions of analytic truth. Reports are labor intensive and inconsistent. Data governance is non-existent. © Healthcare Analytic Adoption Model HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 71
  68. 68. Healthcare Analytic Adoption Model Level 8 Cost per Unit of Health Reimbursement & Prescriptive Analytics Contracting for & managing health. Customizing patient care based on population outcomes. Level 7 Cost per Capita Reimbursement & Predictive Analytics Diagnosis-based financial reimbursement, managing risk proactively, measuring true outcomes Level 6 Cost per Case Reimbursement & The Triple Aim Procedure-based financial risk and applying “closed loop” analytics at the point of care Level 5 Clinical Effectiveness & Population Management Measuring & managing evidence based care Level 4 Automated External Reporting Efficient, consistent production; agility, and governance Level 3 Automated Internal Reporting Efficient, consistent production; widespread access to KPIs Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data Level 1 Integrated, Enterprise Data Warehouse Foundation of data and technology Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. WangCopyright © 2016 Frank F. Wang 72

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