Improving Population Health through Interprofessional Leadership: The Case for a Professional Practice Model

Tuesday, 10 November 2015: 10:40 AM

Melanie Brewer, DNSc, MS, BSN, FNP-BC, FAANP1
Maria O'Rourke, PhD, RN, FAAN1
Jeffrey Francis, BSN, RN2
(1)Center for Nursing Excellence, Scottsdale Lincoln Health Network, Scottsdale, AZ, USA
(2)Department of Nursing, Scottsdale Healthcare, Scottsdale, AZ, USA

A professional practice model is foundational to the development of a results-oriented clinical enterprise (O’Rourke & White, 2011).  This model draws attention to the need for clinicians, managers, and other members of the healthcare team to work together synergistically as interprofessional leaders in order to ensure sustainable improvements in patient outcomes.  The model also serves as the structure for evaluating patient care improvements based on the decision making actions of clinicians and managers.  Each element of the professional practice model is an important link to organizational effectiveness and each must be evaluated as to the extent of its impact.  In the era of the Affordable Care Act, population health is linked to improving the quality of care (Stoto, 2013). This makes population health an important element of a professional practice model and emphasizes the need for population health management to be viewed as essential to organizational effectiveness. Healthcare informatics is a critical aspect of defining the patient population served. This presentation focuses on population health as a key driver for understanding the link among diagnostic related groups (DRGs) and comorbidities, clinical practice, and outcomes, specifically nurse sensitive indicators.  

In a three-hospital tertiary health care system (875 total beds), a major initiative to improve outcomes through a more refined use of health informatics was undertaken. The initial work of building the template for analysis was conducted at the largest of the system facilities with 430 beds.  In collaboration with members of the clinical analytics team, DRG and comorbidity data were examined through an analysis of administrative and quality outcomes data from Horizon Performance Manager by McKesson and the Midas Plus Care Management system.  Patient profiles were developed that aligned the discharge DRG with associated comorbidities on a hospital-wide and then on a unit-specific basis.  This study explores the link between the patient profile using DRGs and comorbidities (ICD-9 codes) and the occurrence of nurse sensitive indicators. Data including 100% of discharged patients for a particular unit for a given 12-month period were used.  Data were then sorted according to the top DRGs and comorbidities to produce a DRG profile for each unit.  DRGs and comorbidities were sorted so as to create a dataset organized into “buckets” to create an 80-20 percent split of the patient population.  The bucketed data set was rank ordered to produce a profile that represented 80 % of the patients on the particular unit. Associated comorbidities were aligned with these DRGs.  The same process of analysis was used for the remaining 20% of the DRGs and comorbidities. This methodology sorts out the patient population that is typical (80 percent)  and consistently seen on each unit.  Level of nurse competency is viewed as high due to the predictability of characteristics of patients admitted to the units.  This research hypothesizes that there is a higher likelihood of poor outcomes on nurse-sensitive indicators due to the existence of patients who make up the 20% of the patient population.

DRG and comorbidity data were summarized for each hospital unit (26 units). Correlations were conducted among the DRGs and comorbidities with nurse-sensitive outcomes for the 80% patient population and links to the nurse-sensitive indicators were evaluated. The same analyses were conducted on the 20% patient population.  The analysis completed on this specific unit was limited on the number of cases with nurse sensitive indicators and therefore was insufficient to determine significance.  The variety of DRGs and comorbidities within the unit was telling and revealed important implications for patient safety, nurse staffing, and patient education. A key outcome of this analysis is the identification of patients with more comorbidities that were identified as part of the case profile.  These cases were associated with falls. This analysis produced micro level data about the patient condition that can now be linked to specific patient populations at the micro rather than macro level. These micro level data are critical as they inform manager and clinician decision making. Analyses on 26 additional units and across the 2 other hospitals are in progress and will be reported out as part of the presentation of this study.

An important finding is that, based on the current health information systems, there may not be a clear cut means to analyze the link of DRG and comorbidity data with nurse-sensitive indicators due to limitations of the existing data in quality and administrative datasets.  This finding is important for clinical analytics improvement as it is vital that they build the capacity to retrieve the correct information from existing data systems in an efficient and useable manner.    

This unique approach to population health and the organization of data that generates a more refined patient profile will serve to improve the quality of care and clinical and management decision-making, decision making that impacts resource allocation for education, staffing, patient placement, RN competency, and interprofessional collaboration.