Using Data Analytics to Validate Data Quality in Healthcare

Transcription

1 Using Data Analytics to Validate Data Quality in Healthcare Sponsored by 1915 N. Fine Ave #104 Fresno CA Phone: (559) Fax: (559) Program Handouts Tuesday, June 9, 2015 Track Two 3:20pm 4:20pm 2015 State Convention and Exhibit Speaker Connie Renda, MA, RHIA, CHDA Copyright California Health Information Association, AHIMA Affiliate

2 California Health Information Association California Health Information Association Using Data Analytics to Validate ld Data Quality in Healthcare Connie Renda, MA, RHIA, CHDA Disclaimer This material is designed and provided to communicate information about health data in an educational format and manner. The authors are not providing or offering legal advice but, rather, practical and useful information and tools to achieve compliant results in the area of data quality and analysis. Every reasonable effort has been taken to ensure that the educational information provided is accurate and useful. Applying best practice solutions and achieving results will vary in each hospital/facility, and testing situation. Copyright California Health Information Association, AHIMA affiliate 1

3 Overview Why is healthcare data quality so important? Data management Steps to ensure data integrity Validating data Data analytics role in HIM Challenges and future goals Why is data quality so important? $600,000,000,000,, (six hundred billion dollars) Annual cost of poor data quality in American businesses according to the Dt Data Warehousing Institute t Copyright California Health Information Association, AHIMA affiliate 2

4 Why is data quality especially important in healthcare? Clinical data is a basic staple of health Creating, protecting patients and public health Right data Right time Right context Right care Instituteof Medicine (IOM) goal: By 2020, 90% of clinical decisions will be supported by accurate, timely, up to date clinical information The electronic health record leads to an increase in clinical documentation which then translates to an increase in the volume of clinical data available. EHR clinical documentation volume of clinical data Copyright California Health Information Association, AHIMA affiliate 3

5 What is healthcare data? Measures of health Lab data Physical exam findings Imaging studies Treatments Rx Determinants of health Biomedical Genetics Demographics Behaviors Socio economic factors Environmental factors Administrative data MPI demographics Coding/billing data Compliance statistics Productivity Marketing information Copyright California Health Information Association, AHIMA affiliate 4

6 Data Quality Management Definition=roles, responsibilities, policies and procedures specifically concerning acquisition, maintenance, dissemination, disposition and destruction of data Data quality 100% accuracywith zero errors Manage and reasonably define for your organization Accurate business decisions Quality data Effective data management Copyright California Health Information Association, AHIMA affiliate 5

7 Data Quality Management Acquisition How are data acquired/entered? Training, standard procedures Maintenance Are storage capabilities adequate? Are regular reports/dashboards available? Dissemination How does data move around organization? Privacy and security must be at forefront Information gathered from disseminated data form as basis for spotting industry trends and patterns and decision making in companies Destruction What is your organization s data retention and destruction policy and procedure? What is the appropriate method for data destruction given the types of data being used by the organization? Does your organization have an ongoing plan (monthly, quarterly) plan for destruction of data? Steps to data integrity Interdisciplinary task force Training, training, training! Centralized, ongoing, review of importance of data integrity; monthly meetings with continuous updates of data definitions, field specifications Reviewed with clinical and clerical staff Copyright California Health Information Association, AHIMA affiliate 6

8 Steps to data integrity Incorporate public reporting elements into EHR or paper documents Specialized tools for ICD requirements Forms usage tracking and enforcement Grid with all data elements (paper based) Required fields with data elements (electronic based) Data validation System reconciliations Find missing documentation and WHY is it missing? Data Quality and Integrity: Cleaning & Validating Primary goal: Data source Focus on the data being entered Registration/admitting/anywhere data is input into the system Secondarygoal: Data validity Determining errors, especially patterns Return to source to correct/reduce repeat errors Copyright California Health Information Association, AHIMA affiliate 7

9 Steps to data integrity: Examples of data errors Data Errors Problem with data field (e.g., no collection field) Migrated data loses information (esp. multiple data sources) Missing data (may be ok for certain fields) Data Format errors Numeric fields which h have letters ltt (e.g., zip code numeric only unless international) Clarity (01 vs. 1) Date formats (mm/dd/yyyy vs. mm/dd/yy) Finding errors Sampling Looking at a portion of the dt data to dt determine if there are errors in entire database 100 records test Visually inspect data set for errors Especially easy to see when entire field is incorrect Can sort using filters with 10 most important fields to narrow down search Copyright California Health Information Association, AHIMA affiliate 8

10 Finding errors (cont.) Mapping or coding errors Gender: Male=1, Female=2, other=3 If not correctly mapped or changed, could cause many errors (difficult to detect and fix) Incorrect dates allowed (e.g., Sept. 31) More clearly defined values=>less errors Solutions to data errors Cleansing Dlti Deleting/flagging/automated i / t tdcorrection Basic errors such as changing M=1; F=2 Correcting Gathering information from another source Such as a paper record with errors in OCR text reading Copyright California Health Information Association, AHIMA affiliate 9

11 Data Validation Right data into right place at right time Why? Quality reporting, such as Meaningful Use, PQRI or eprescribing reimbursement incentive programs Benchmarking Bottom Line! Are the data extracted from the system accurate, reliable and relevant? Data validation elements Credibility Trustworthy th results Completeness All valid codes entered? Reasonability Unexpected spikes/changes Consistency 78 year old man cannot have pediatric values Copyright California Health Information Association, AHIMA affiliate 10

12 Some indicators of success ICD 10 Mapping ICD 9 data to convert correctly Clinical ldata Abstraction Center (CDAC) Requires 80% accuracy (strive for 95%) Registries Require different levels of accuracy Reports Noncompliance, adverse effects, scorecards and dashboards (internal and external) JC core measures OASIS, HEDIS, MU Data analytics in HIM Proactive Overall governance Data dictionary Defining roles Quality expectations Accuracy rates Supporting business practices Productivity Technical environments Software interfaces with EHR vendor Reactive Problems inherent in data (MPI cleanup) Data in legacy systems Pre data quality Mergers/centralizations Combining data Reducingexisting existing data problems Copyright California Health Information Association, AHIMA affiliate 11

13 Managing the data has its challenges Copyright California Health Information Association, AHIMA affiliate 12

14 Data quality challenges Not my problem No business unit feels the data quality is their responsibility It must be an IT issue IT can only ensure it operates correctly ITcan t makerules about data needs HIM Interdisciplinary project Data quality challenges (cont.) We don t have data issues denial of problems requires organization to admit they have problems often takes a major catastrophe to bring problems into light Financial investment often difficult to justify cost requires discipline personnel to staff functions of data stewardship Copyright California Health Information Association, AHIMA affiliate 13

15 Data quality challenges (cont.) ROI difficult to quantify mostly preventative/proactive can t quantify costs if you don t know there are errors may be a long time before cost savings can be proven/realized or patient safety issue is resolved Challenges can often be overcome by finding other hospitals that have had problems/financial losses due to poor quality or erroneous data Copyright California Health Information Association, AHIMA affiliate 14

16 Why aren t we using the data? Barriers Technical Standard vocabularies Storage space/capabilities Cultural Stakeholders Privacy issues Legal Data ownership Emerging HIM data roles Data validation development Data steward Manages EHR data as a corporate asset Data analyst Translates business requirements into data models, acquisition and delivery Develops data definitions and conveys to development and IT teams Cleans and validates data Copyright California Health Information Association, AHIMA affiliate 15

17 Sit at the head of the table Make a dff difference! Copyright California Health Information Association, AHIMA affiliate 16

18 Questions? Contact information: Connie Renda, MA, RHIA, CHDA Assistant Professor and Program Director Mesa College References data validation will make your life easier rs09084.pdf Information Governance Copyright California Health Information Association, AHIMA affiliate 17