Case study

a data-driven approaCh: improving outComes and FinanCial perFormanCe

BaCkgroundIt can be argued that never before in the history of healthcare has the pressure been so great to simultaneously improve both the quality of care and the financial parameters surrounding the delivery of care. Indeed, as these pressures have grown, much attention has been turned to potential solutions. From pay-for-performance and disease management, to electronic medical records and health risk assessments, today and tomorrow’s promising saviors of the healthcare quality and cost dilemma have a common trait throughout – empowerment through healthcare information technology.

Indeed, the crucial requirement for timely access to granular, comprehensive, and accurate patient data is hardly disputed. Yosef Dlugacz, Ph.D., director of the Julienne and Abraham Krasnoff Center for Advanced Studies in Quality and a supervisor of the development of many best-practice quality standards applied by JCAHO to the entire health care industry, documents well the role of access to quality medical data to assess, monitor, and improve care, verify application of evidence-based medicine, eliminate communication gaps, achieve consistent integration of care, promote collaboration among providers, optimize financial performance, and improve safety both for individual patients and across systems.1 Chaudry, et al reviewed the evidence on the impact of the application of health information technology on health care quality and efficiency, and in so doing demonstrated improved adherence to published guidelines, increased surveillance and monitoring, fewer medication errors, and decreased utilization of care.2 As would be similarly expected, the work of several authors (such as Peter Smith et al and Darryl McDonald) has supported a link between poor electronic data quality and medical errors as well as overall substandard care.3,4 Furthermore, in numerous studies, Bates, et al has argued that quality information technology can improve individual patient safety, improve access to reference information, enable “smart” patient monitoring to allow early recognition of trends indicating decompensation, and identify adverse events and track their frequency.5

Nevertheless, despite the myriad of evidence supporting the applications of information technology to realize valuable healthcare quality improvements and financial performance benefits, many barriers unfortunately stand in the way of achieving ubiquitous availability of appropriate data.

Overcoming these barriers, however, first requires an improved appreciation for the various mediums within which the comprehensive healthcare datasets reside. Administrative data – a term referred to in the healthcare industry commonly constitutes a digital (i.e. already in a form of ones and zeros) representation of laboratory, encounter, pharmacy, durable medical equipment, and other data types of medical data. Unfortunately, while there exist not only the aforementioned challenges with respect to this administrative data, it is typically seen to represent less than 15% of the overall medical data landscape. The balance resides within non-searchable analog sources (e.g. paper medical records, study report mediums such as radiological imaging films, etc.), poorly integrated or non-searchable electronic medical records, or patient-side sources (e.g. indicators of compliance, social support network, and ADL capabilities, etc.).

initiativeThrough the application of a complex set of processes, working together with major healthcare provider networks, an initiative was undertaken to identify and overcome data barriers and dramatically improve the comprehensiveness, granularity, accuracy, and appropriate accessibility of pertinent patient healthcare data within a major metropolitan area. The result was a patient medical data “superset” capable of guiding advanced-generation care management, outcomes, risk adjustment, and financial performance improvement processes.

Utilizing a patient data superset strategy of expanded medical data accuracy, comprehensiveness, granularity, and accessibility to drive real improvements in patient care management, utilization, and financial performance.

MedAssurant, Inc. | 4321 Collington Road | Bowie, Maryland 20716 USA | www.medassurant.com | 800-390-3180

Turning Data

Into Insight, And

Insight into Action™

MedAssurant, Inc. is a leading medical informatics service provider focused on the importance of healthcare data and its ability to drive dramatic, objective improvements in clinical and quality outcomes, care management, and financial efficiencies throughout the healthcare community. Proprietary healthcare datasets, abstraction, and analysis capabilities, combined with a national infrastructure of leading-edge technology, clinical prowess, and deep human resources empowers MedAssurant’s advanced generation of healthcare assessment and improvement through highly informed solutions. In partnership with many members of the healthcare community, MedAssurant provides local, regional, and nationwide health insurance plans, hospitals, pharmaceutical companies, regulatory bodies, government organizations, physician organizations, and their many patients with powerful turnkey solutions to address matters of clinical outcomes analysis, quality of care, cost improvement, risk adjustment, disease management, utilization, and healthcare data verification. Founded in 1998, MedAssurant provides services to nearly 200 healthcare organizations in all 50 states, Puerto Rico, and the District of Columbia, and has approximately 1800 employees. Driven by a mission to improve today’s healthcare landscape, the employees of MedAssurant proudly apply care, ingenuity, and dedication to delivering a new approach to healthcare – one driven by data and insight – one resulting in meaningful action.

The initiative began with implementation of a common patient administrative data warehouse containing years of patient encounters, diagnosis, pharmacy, laboratory, durable medical equipment use, practitioner, procedural, hospitalization, financial, risk status, and demographic data. Collaborated efforts lead to improving data feeds from internal health plan IS departments, partnered healthcare organizations, and third-party vendors. The integration, cross walking, and mapping of multiparty data to this comprehensive administrative dataset marked the first step in the data superset initiative. Complex analysis thereafter utilized the amalgamated administrative data to identify “gaps” in data believed to be pertinent, yet outside of the available administrative data. Further analysis enabled the determination of gap prioritization, potential impact to care, quality, or risk status, and probability of substantiating such gaps. In this way, analysis was ultimately able to utilize available data to precisely target (in a prioritized, ROI-conscientious fashion) the location and likely existence (and projected potential value) of additional data not located within the available administrative dataset, yet otherwise needed to construct a highly valuable patient medical data superset. Prioritized gap elements were then confirmed through various forms of highly targeted substantiation and verification through data cleaning, medical record abstraction, and both telephonic and in-person health risk assessment (HRA) application efforts. The result was a highly valuable patient medical data superset – constructed at rapid pace and with significant cost conscientiousness – able to then inform the assessment and improvement of quality outcomes, care management, and financial performance.

resultsData integrity, comprehensiveness, and accuracy were shown to improve significantly within months of implementation. Improved data accuracy further drove the efficiency of resolving continued outstanding data concerns. A myriad of patient, provider, and payer benefits became readily apparent. Patients who previously were unidentified as having specific disease such as diabetes, heart failure, and coronary artery disease were successfully enrolled into applicable disease management programs. Disease management programs were able to be highly stratified and honed to precise custom care management needs and disease states of individual patients. Potentially wasteful and confusing overlap between care management initiatives were reigned in. Improved data indicating past medical history, treatments, and diagnostics were made available to appropriate care providers – accurately and in more timely fashion than previously possible. Guides regarding important disease follow up and areas of patient compliance were identified. More accurate baseline measures, quality outcome analysis, risk adjustment, and patient needs assessments were able to be accomplished.

In an examination of over 4,500 patients (each with approximately 2.2 diagnostic assessment gaps per patient), MedAssurant’s ePASS™ solution facilitated the closure of 61.8% of such diagnostic assessment gaps in comparison to only 3.7% in cases not exposed to ePASS™. Furthermore, as a measure of utilization decrease, emergency room presentations dropped by 25.5% following the implementation of ePASS™ for those utilizing the solution. This is in comparison to a 6.3% drop for non-participating patient cases. Further still, with improvements in disease identification, assessment, documentation, and data accuracy, risk adjusted reimbursement accuracy improvements led to significant financial improvements.

In an examination of over 35,000 patients (with complex medical cases involving diabetes, heart failure, and coronary artery disease), MedAssurant’s CCS Advantage™ data-driven disease management program witnessed a 21% increase in LDL compliance, a 25.13% increase in HgbA1c compliance, and a significant decrease in lower extremity complications such as cellulitis (which decreased by 16.6%) amongst members engaged in MedAssurant’s program for six months or longer. Acute presentations also dropped significantly with hospital presentations dropping 45% and 48% for diabetes and heart failure respectively. The cost of care for the same patient group decreased by approximately 15.9% in comparison to the same patient’s costs during the six months prior to program engagement.

1 Dlugacz,Y.D., Measuring Health Care: Using Quality Data for Operational, Financial, and Clinical Improvement. San Francisco: Jossey-Bass, 20062 Chaudry, B., et al, “Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care,” Annals of Internal Medicine, May 16, 2006 (144:10), 742-752.3 Smith, Peter et al., “Missing Clinical Information During Primary Care Visits,” JAMA, Feb. 2, 2005 (293:5), 565-571.4 McDonald, Darryl, “Data Quality Management: Oft-Overlooked Key to Affordable, High-Quality Patient Care,” Whitepaper (7/17/2004) at HCT Project, Volume 2, p.1.5 Bates, D., et al “Reducing the Frequency of Errors in Medicine Using Information Technology,” J Am Med Inform Assoc 2001; 8: 299-308.

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