Getting Started with Data Governance

Transcription

1 Getting Started with Data Governance Gregory S. Nelson, MMCI, CPHIMS ThotWave Technologies, LLC

2 3 Maturation Data Quality Data Access Data Integra6on Governance + MDM Data Management 4 2

3 Data Governance refers to the organizational focus that helps to ensure confidence around data and how it is defined in the context of the business its use, interpretation, value and quality is managed. 5 Data Quality SAS Data Quality Desktop SAS Data Quality Standard/Advanced SAS Data Quality Accelerator for Teradata Data Integra0on/ Management SAS Data Integra6on Server SAS Data Management Standard/Advanced Data Governance SAS Data Governance Master Data Management Master Data Management Standard/Advanced 6 3

4 CMS Meaningful Use 1 Master Pa0ent Index Master pa6ent index that uniquely iden6fies all pa6ents of a hospital or eligible provider 2 3 Master Provider Index Master provider index that uniquely iden6fies all providers across an organiza6on, even if they traverse care sites Standards Adherence Appropriate and consistent usage of standards to be able to meet interoperability requirements 7 Pop Health Geospatial Analytics Address Types Business defini6ons and collec6on processes for different address types of pa6ents and care loca6ons. Pa0ent Address Process to define best guess of a pa6ent s current address as well as archive address history. Address Verifica0on Process to verify addresses in real- 6me against USPS or country- specific address sources Precision Defini0ons Defini6on of geocoding precision acceptable for each analy6c ac6vity (e.g. tax parcel centroid, roowop, street interpola6on, zip+4) Geocoding Standards Defini6on of processes to geocode new informa6on as it is received 8 4

5 Chronic Populations Cohort Defini0ons Structured electronic health record data for all chronic disease cohorts Disease Defini0ons Business defini6ons that describe each chronic disease group in terms of what can be queried electronically. Real Time Event Process A scheduled, automated process by which chronic disease cohorts are updated Business Rule Defini0ons A process by which to review defini6ons and update as medical knowledge grows over 6me Pa0ent Reported Outcomes The ability to collect structured pa6ent reported outcomes and family history data Outcomes Defini0ons Business defini6ons for agreed upon outcomes to monitor (e.g. readmission, length of stay, emergency department visit volume) 9 What if? What could you accomplish if you knew you had the right data and could trust that it was accurate? 10 5

6 What would you do? Healthcare Forecast emergency room visits Model equipment inventory and staffing needs using RFID tagging Notify study coordinators of potential recruits in real time Evaluate the impact of different health system initiatives on provider productivity Fine tune readmission rate models 11 4 Principles of Data Governance Data must be recognized as a valued and strategic enterprise asset Data must be managed to follow internal and external rules and regula6ons Data must have clearly defined accountability Data quality must be defined and managed consistency across the data lifecycle Non Invasive Data Governance (Seiner 2014) 12 6

7 Measuring Success: Metadata Technical Business Data flow metrics (# documented, monitored Data dictionary (# terms, # search term lookups) Orphaned data assets (unused reports, data elements) RT in determining whether a question can be answered % data elements where the following can be done on demand: a) visualize the lineage of a data item b) assess the downstream impact on other data elements if something changes Number of data elements with an agreed upon business definition 13 Measuring Success: Content Management Technical Business Percentage of content that has been digitized Percentage of unstructured metadata populated in the metadata repository Number and type decisions that are made (reinforced) with text analytics Latency (in hours) to make a decision with contextual information 14 7

8 Measuring Success: Archiving Technical Business Total storage (in GB) Total Cost of storage Application response time A measure of our ability to reproduce historical reports and the context under which that was true 15 Measuring Success: Security/Privacy Technical Business Ratio of breeches by the total number of cyber attacks Total number of records lost to breeches, negligence Reputational risk given data loss Turnaround time for securely sharing data with researchers and other partners 16 8

9 Measuring Success: Metadata Technical # of reports # of data sources integrated # of business elements defined Ratio of new report requests by those completed Business Number and type of decisions that could be made given the integrated, high quality data (accuracy, validity, completeness, integrity) How quickly one can answer a new or novel question (timeliness) The extent to which analytic results can be reproduced across people and time and the consistency/ reliability of the interpretations The velocity by which new talent can understand and utilize the enterprise s information assets 17 Return on Investment Cost Avoidance Reduction in duplication of technology work/ activities; creation of assets that are unimportant/ unused; duplication of compliance related activities/ controls, elimination of duplicate systems/ technology (infrastructure, applications, data feeds). Wasted Time/ Improved Time to Decision two sides of the same coin that can include waiting for data, spending time on non-value creating activities, churning through data quality issues or time spent finding and acquiring data. Improved Communication/ Collaboration Cost of Poor Quality as data is recognized as an enterprise asset, others can benefit from the sharing of information, analytic results and knowledge about the interpretation and impact on action. poor decisions that were made based on inadequate or inaccurate data; reductions made in erroneous decisions. Improvement of Actionable Models improvement in analytic models and resulting impact/ decisions made (e.g., demand management forecasting and their impact on staffing models and patient volume, readmissions rate model targeting, utilization, etc.) 9

10 Capabilities Defined how should we organize ourselves? what technologies can be applied and in what order should be acquire them? what talent / skills do we need and should we buy or build (contract versus grow)? 19 Conclusion Mindset Skillset Toolset How the organization sees, perceives, and views data governance Describes the core set of beliefs, a consistent approach to prioritization, and realistic expectations of what can be accomplished Do, Act, Behave Skills and behaviors required to deliver data governance How you execute on the vision Using a set of widely accepted methods, techniques, models, approaches, and frameworks that can create value

11 Contact Gregory S. Nelson, MMCi, CPHIMS ThotWave Technologies, linkedin.com/in/thotwave thotwave.com 11