5 Reasons Master Data Management (MDM) is Critical to The Success of a Data Driven Health System & Accountable Care Organization (ACO)

Transcription

1 5 Reasons Master Data Management (MDM) is Critical to The Success of a Data Driven Health System & Accountable Care Organization (ACO)

2 1. Prologue There s no denying that people are an organization s #1 asset, but data is a close second. Today s health systems and ACOs rely on large, aggregated data sets to guide both their clinical process and business strategy. Yet, a recent CHIME survey found an 8%, and in some facilities up to a 20% error rate due to patient mismatching. 1 Without a means for accurately identifying, matching and managing patient data across multiple source systems, providers risk not only data integrity, but also, and more importantly, the safety of their patients. Forward-thinking IT leaders are looking beyond traditional EMPI solutions to improve cost, quality and outcomes. In this paper, you ll learn why Master Data Management (MDM) capabilities, including matching, cleansing, governance and visualization have emerged as essential characteristics of a data-driven healthcare organization. MDM is recognized as a required discipline by more-advanced and more-complex HDOs with EHR systems in place, and integrated clinical or business EDW and population health management initiatives underway. Gartner Hype Cycle for Healthcare Provider Technologies and Standards (July 24, 2014) PAGE 2

3 1. Population Health Management While hospital leaders rely on complete and accurate data to establish a population health management strategy, it is clinicians and care managers who rely on this data to coordinate care. Missing or inaccurate information can impede care management programs, resulting in gaps in care, readmission or patient harm. Successful population health strategies require a single, longitudinal view of patient data across an enterprise. A mature MDM solution produces a golden record for any specified data entity; thus eliminating the labor intensive and, often costly, practice of establishing a pre-defined data model. Through sophisticated matching and cleansing, MDM technology delivers a single, accurate and longitudinal view of the data. This enables clinical and business leaders to build relationships and identify trends across patients, providers, facilities and locations. 2. Big Data Analytics The healthcare industry is poised to be the primary driver of growth in the worldwide big data market 2 a prediction that comes as no surprise to health IT professionals tasked with managing large, complex data sets on a daily basis. Yet, despite the projected growth, a recent KPMG study found 70% of healthcare organizations lacking confidence in their data due to challenges with data quality and reliability 3 Healthcare IT leaders must quickly find a way to close the gap between simply collecting data and utilizing it as a strategic asset. MDM serves to lay the foundation for consistent and reliable data across an enterprise. For the majority of healthcare organizations, consolidating data from multiple sources is required for analytics. Take for example patient Joe Smith whose data exists in both an inpatient and ambulatory EMR each run by separate vendor. In order to conduct patient-centric analytics, a single view of Joe Smith is required, but which system is more accurate, and which data model do you use? MDM technology strategically federates this data without the need for a pre-defined data model; thus, enabling multiple stakeholders clinical, research, financial, operational to achieve a single view of Joe Smith. By eliminating variability and ensuring analytic consistency, organizations can better identify trends, monitor patient behavior, track provider performance and, ultimately, gain a competitive edge. 3. Enterprise Data Warehouse Health systems and ACOs are turning to enterprise data warehouses (EDWs) to aggregate and organize data across the enterprise. At their core, EDWs offer insight into organizational data and reporting functionality across financial, administrative, clinical, and research functions. Yet, they lack data governance and stewardship capabilities that protect data quality. control when to accept changes and which source provides the most trusted update. With multiple systems feeding an EDW, and data changes occurring on a regular basis, it is essential to prioritize a data governance process and empower Data Stewards with the tools they need to identify a trusted source, accept or dismiss a change, and distribute that change back to the source systems. PAGE 3

4 4. Patient Engagement Meaningful Use Stage 2 (MU2) requires providers to demonstrate measureable achievements in patient engagement; more specifically, a 5% utilization rate of patient portals. Compliance with MU2 hinges upon the availability and accessibility of patient data in a portal. To increase utilization rates, providers are looking to integrate multiple source systems to create a single record that displays accurate data to the patient. between multiple source systems inpatient, ambulatory, patient portal. This approach ensures that each system is accurate, consistent and displaying data in real-time. In addition, MDM can establish parameters for patient-entered data, enabling only trusted data to be synchronized with the systems. Clean and complete data will become the silver bullet of patient engagement. 5. Patient Safety & Quality Experts estimate an 80% accuracy rate of patient identity matching in today s hospitals or a 1 in 5 chance that a patient s safety is at risk. A recent ONC report indicated that errors in patient ID and matching can lead to misinformation at the point of care and thus, serious treatment consequences including a preventable medical error and/ or risk of death. 4 With over 400,000 patient s dying each year from preventable medical error, data accuracy should be every healthcare professional s #1 priority. inconsistent information, and establishes a single, trusted source of information across the enterprise. Regardless of the care setting (e.g., acute or ambulatory), MDM integrates and manages data so as to deliver a longitudinal record for patients and/or providers which enables the delivery of safe, high-quality healthcare at every instance. Putting accurate information in the hands of both clinical and business leaders is achievable with MDM. PAGE 4

5 Customer Spotlight Maury Regional Medical Center (Columbia, TN) implemented VisionWare s MultiVue solution with the primary goal of matching, merging and cleansing patient data from different inpatient and outpatient EMRs to obtain a single source of truth in the patient portal. MultiVue s scalability, low TCO and collaborative data model, paired with its data governance and visualization capabilities, has enabled Maury s leadership to: Establish a 360 view of patient and provider data across inpatient and ambulatory facilities Enhance the patient portal offering to drive patient engagement Ensure the availability and visibility of high quality data for use in population health management and care coordination The need for reliable and accurate data across each source system is imperative. MultiVue s collaborative data model ensures that we can maintain the highest level of data integrity, regardless of the original data source or format. Jim Parcel CIO, Maury Regional Medical Center Maintain outstanding quality ratings in a highly competitive market PAGE 5

6 References 1 - College of Healthcare Information Management Executives (CHIME) Survey on Patient Data Matching (May 16, 2012) 2 - Transparency Market Research Big Data Market - Global Scenario, Trends, Industry Analysis, Size, Share and Forecast (May 28, 2015) 3 - KPMG Going Beyond the Data: Turning Data from Insights into Value (July 2015) 4 - Office of the National Coordinator for Health IT Patient Identity and Matching Final Report (February 2014) PAGE 6

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