Building an Analytic Infrastructure for Clinical Informatics: A Primer for Home Healthcare Agencies

Transcription

1 Building an Analytic Infrastructure for Clinical Informatics: A Primer for Home Healthcare Agencies Robert J. Rosati, PhD Timothy Peng, PhD David Russell, PhD Prepared for the 31 st NAHC Annual Meeting & Exposition, October 21-25, Orlando, Florida 1 What is this workshop about? Opening the audiences eyes and ears to the possibilities inherent within routinely collected clinical and administrative data Sharing a framework with important steps for starting to use these data with the aim of answering important questions about the quality of home healthcare services 2 1

2 What is this workshop not about? Specifications of Information Technology systems in home health care Standalone data mining techniques and processes 3 Defining Clinical Informatics Clinical informatics is a multidisciplinary field focusing on technology applications that address the collection, processing, and representation of clinical data Designing and implementing information and communication systems that: Improve patient care and outcomes Strengthen the clinician-patient relationship 4 2

3 Clinical Informatics in Home Health Health Information Exchange Clinical Decision Support Quality Measurement 5 Defining Analytics Analytics is the application of information technology, operations research, and statistics to solve problems in business and industry Analytic applications use: Data Advanced multivariate modeling Predictive modeling Other statistical techniques 6 3

4 The Analytic Perspective Sanders (2002) describes three stages of an organizational analytic perspective Retrospective What happened in our past? Learning Why did it happen? Predictive What will happen and when? 7 Examples of Analytics Identifying treatment regimens that yield the best outcomes for patients Exploring the risk factors for adverse events and negative outcomes 8 4

5 Analytics in Home Health Home Health Compare CMS CASPER Reports Vendor applications 9 Visiting Nurse Service of New York Founded in 1893 by Lillian Wald Largest not-for-profit home care agency in the United States 30,000 patients/day Mission to promote the health and well- being of patients t and families by providing high-quality cost-effective care in the home and community 10 5

6 VNSNY Service Regions 11 Using Technology at VNSNY Early pioneer in the use of technology Electronic medical records Tablet computers Remote transmission of clinical assessments Patient information database Web-based based intranet reporting 12 6

7 VNSNY Outcomes Initiative Launched in 2001 Agency-wide system for collecting and processing patient data, with the aim of using it to improve quality of care and patient satisfaction Three aims of the Outcomes Initiative: Reporting Analytics Evaluation 13 VNSNY Technology Video 14 7

8 Developing a Framework Through our experiences, we have identified a five phase framework for building clinical informatics and analytics capacity within home care agencies 15 Five Phases of the Framework Phase I: Developing a Strategic Plan Phase II: Basic Requirements Phase III: Building a Data Warehouse Phase IV: Reporting Templates Phase V: Advanced Analytics 16 8

9 Phase I: Strategic Plan The development of a strategic plan requires vision and leadership Understanding how clinical data can be harnessed to provide: A foundation of knowledge about patient characteristics and service use Insight into new trends, policy changes, and business opportunities Support from senior management 17 Phase I: Strategic Plan (continued) Identifying an appropriate project scale Large scale Small scale Organization size and budget constraints Consideration of how a plan for developing clinical informatics and analytics capabilities should be scaled to the size and budget of an organization 18 9

10 Phase I: Strategic Plan (continued) Assembling a project team with appropriate knowledge and skills Information technology Analytics Clinical experts Project implementation ti Assembling an internal team of staff Hiring a team of external consultants 19 Phase I: Strategic Plan (continued) Business Impact High Low Low Complexity High 20 10

11 Phase II: Basic Requirements These capabilities are show-stoppers Electronic data collection methods Networks and computers for processing patient data Procedures to ensure security, integrity, and consistency of data collection Accessibility of patient data 21 Phase III: Data Warehousing Creating a centralized data repository that integrates data sources across multiple information systems: Electronic Medical Record Plan of care OASIS Extraction, Transformation, and Load (ETL) model 22 11

12 Phase III: Data Warehousing (cont d) Identifying the business requirements and specifications for the data warehouse Definitions and structure of the warehouse 23 Phase IV: Reporting Templates Identify audience and stakeholders Agree upon an organization and format Identify and define metrics 24 12

13 Phase IV: Reporting Templates Specify interactive features Drilling down or rolling up How can clinicians use this information? Adoption among users of the reports 25 Example: Quality Scorecard Balanced Scorecard approach Robert Kaplan & David Norton (1992) Quality and performance indicators Process (Ex. Care Planning) Utilization (Ex. Visits per Episode) Outcomes (Ex. Hospitalization Rate) Patient Feedback (Ex. Satisfaction with Care) 26 13

14 VNSNY Quality Scorecards (continued) 27 Example: Who are our Patients? VNSNY Who are our Patients? Programs and regions Demographic and payer characteristics Service and clinical characteristics Annual written report Interactive report 28 14

15 VNSNY Who are our Patients? 29 Example: Transitional Care Transitional care refers to the continuous and coordinated transferring of patients from one care setting to another Transitional care has been a focus at VNSNY Heart Failure Transitions Program Partnership between VNSNY and a regional hospital 30 15

16 Heart Failure Transitions Program SOURCE: Russell et al (2011) 31 Heart Failure Transitions Program 32 16

17 Phase V: Advanced Analytics Developing an analytic perspective Evidence-driven organizational culture Emphasizing process change and continuous quality improvement Understanding the bigger picture Well-utilized clinical data warehouse Collection and synthesis of data from various transaction-based sources 33 Phase V: Advanced Analytics (continued) Integration of Algorithms & Modeling into Usual Practice (e.g., Sanders, 2002) Discovery of what happened Understanding why Identifying opportunities, forecasting, anticipating what will happen Formulating answerable questions Mastering your data Applying the correct analytic techniques Understanding the inferences you can make 34 17

18 Analytics Example Heart Failure Transitions Program 35 Heart Failure Transitions Program Question Did a transitional care program improve key patient outcomes? Data for retrospective evaluation: Outcomes (30-day hospitalization) Key measure (transitional care intervention, against matched controls) Risk-adjustment (clinical, demographic, and administrative risk factors at SOC) 36 18

19 Heart Failure Transitions Program Analytic approach Binary outcome: hospitalized or not Multivariate risk-adjustment Logistic regression to determine the relative odds of hospitalization for intervention patients vs controls 37 Heart Failure Transitions Program SOURCE: Russell et al (2011) 38 19

20 Analytics Example Continuity of care 39 Continuity of Care Patient-provider relationships that are connected and coordinated across time and setting Providers are connected to their patients through ongoing, accurate observation of their health status The services patients receive are coordinated by providers through routine monitoring and evaluation of care plans 40 20

21 Continuity of Care Identifying a continuity of care measure s 2 j n j n = 1 COC = n( n 1) Patient A: EEEEEEEEEEEEEEE = 1.00 Patient B: EEEEFFEEEFFEEEE = 0.58 Patient C: EEEEFFFGGHHHJJJ = Continuity of Care Question Is this measure of continuity related to important patient outcomes? Data for retrospective analysis Outcomes (hospitalization, ED use, OASIS functional improvement) Key measure (continuity of nursing care) Risk-adjustment (clinical, demographic, and administrative risk factors at SOC) 42 21

22 Continuity of Care Analytic approach Binary outcomes Multivariate risk-adjustment to isolate effect of continuity Logistic regression to calculate predicted probabilities of outcomes occurring as a function of continuity: Hospitalization ADL Improvement ED use 43 Continuity of Care SOURCE: Russell, Rosati, Rosenfeld, Marren (2011) 44 22

23 Continuity of Care Follow-up Question What factors are associated with lower continuity? Data for retrospective analysis Outcome: calculated continuity of nursing care Candidate predictors clinical, demographic, administrative and service utilization factors 45 Continuity of Care Analytic approach Binary outcome: Low continuity Multiple candidate indicators of association Logistic regression to determine the association of candidate indicators to continuity 46 23

24 Continuity of Care Twice Daily Visits Home Health Aide Weekend Visits Patients who received twice-daily nursing visits are two times more likely to have lower continuity Patients with a home health aide are 9% less likely to have lower nursing continuity than patients without Patients who received > 20% of their visits on the weekend are five times more likely to have lower nursing continuity 47 Continuity of Care Quality Improvement: COC Scorecard Metric Target Monthly YTD 48 24

25 Analytics Example Service Utilization 49 Service Utilization Questions What patient factors drive Skilled Nursing utilization? Can we predict at the start of care how much home health service a patient will need? Data needed to build predictive model Demographic, clinical, and administrative data available at start of care 50 25

26 Service Utilization Analytic approach Model dependent variable is a discrete count event: Nursing visits Volume of visits is related to exposure to home health (LOS) Poisson regression using log e (LOS) as offset parameter to predict expected nursing visits per day 51 Service Utilization Selected major predictors Wounds, Ulcers Abnormal Gait Dx Injectables Req Asst Stable Status Arthritis Dx 52 26

27 Example: Predicting Utilization 53 Foundation for Advanced Analytics Leadership Agency culture Existing Infrastructure & Resources Cooperation, expertise, and engagement of staff from IT, clinical experts, and research analysts Data collected and warehoused with integrity Tools for analysts Tools for users 54 27

28 Summary A generalized framework for utilizing clinical informatics and analytics within home health organizations Opportunities Greater understanding of complex issues Potential ti to improve quality and process Challenges Substantial organizational investments 55 Lessons Learned It s complicated Investment is significant (time and money) Finding the right talent is challenging Translating analytics into action is exciting Demand for analytics can exceed capacity Payoff does exceed the cost 56 28

29 Additional Resources and Information VNSNY Center for Home Care Policy & Research: Russell, D., Rosati, R.J., Rosenfeld, P., & Ames, S. (2010). Using technology to enhance the quality of home health care: Three case studies of health information Technology initiatives at the Visiting Nurse Service of New York. Journal for Healthcare Quality, 32, Russell, D., Rosati, R.J., Sobolewski, S., Marren, J., & Rosenfeld, P. (2011). Implementing a transitional care program for high-risk heart failure patients: Findings from a community-based partnership between a certified home healthcare agency and regional hospital. Journal for Healthcare Quality, 33, Russell, D., Rosati, R.J., Rosenfeld, P., & Marren, J. (2011). Continuity in home health care: Is consistency in nursing personnel associated with better patient outcomes? Journal for Healthcare Quality, 33,