Perceived performance and impact of a non-physician-led interprofessional team in a trauma clinic setting.

Faced with the challenge of meeting the wide degree of post-discharge needs in their trauma population, the University of Pittsburgh Medical Center (UPMC) developed a non-physician-led interprofessional team to provide follow-up care at its UPMC Falk Trauma Clinic. We assessed this model of care using a survey to gauge team member perceptions of this model, and used clinic visit documentation to apply a novel approach to assessing how this model improves the care received by clinic patients. The high level of perceived team performance and cohesion suggests that this model has been successful thus far from a provider perspective. Patients are seen most frequently by audiologists, while approximately half of physical therapy and speech language therapy consults generate a new therapy referral, which is interpreted as a potential change in the patient's care trajectory. The broader message of this analysis is that a collaborative, non-hierarchical team model incorporating rehabilitative specialists, who often operate independently of one another, can be successful in this setting, where patients appear to have a strong and previously under-attended need for rehabilitative intervention.

New chronic disease medication prescribing by nurse practitioners, physician assistants, and primary care physicians: a cohort study.

BACKGROUND: Medications to treat and prevent chronic disease have substantially reduced morbidity and mortality; however, their diffusion has been uneven. Little is known about prescribing of chronic disease medications by nurse practitioners (NPs) and physician assistants (PAs), despite their increasingly important role as primary care providers. Thus, we sought to conduct an exploratory analysis to examine prescribing of new chronic disease medications by NPs and PAs compared to primary care physicians (PCPs). METHODS: We obtained prescribing data from IMS Health's Xponent™ on all NPs, PAs, and PCPs in Pennsylvania regularly prescribing anticoagulants, antihypertensives, oral hypoglycemics, and/or HMG-Co-A reductase inhibitors pre- and post-introduction of five new drugs in these classes that varied in novelty (i.e., dabigatran, aliskiren, sitagliptin or saxagliptin, and pitavastatin). We constructed three measures of prescriber adoption during the 15-month post-FDA approval period: 1) any prescription of the medication, 2) proportion of prescriptions in the class for the medication, and 3) time to adoption (first prescription) of the medication. RESULTS: From 2007 to 2011, the proportion of antihypertensive prescriptions prescribed by NPs and PAs approximately doubled from 2.0 to 4.2 % and 2.2 to 4.9 %, respectively. Similar trends were found for anticoagulants, oral hypoglycemics, and HMG-Co-A reductase inhibitors. By 2011, more PCPs had prescribed each of the newly approved medications than NPs and PAs (e.g., 44.3 % vs. 18.5 % vs. 20 % for dabigatran among PCPs, NPs, and PAs). Across all medication classes, the newly approved drugs accounted for a larger share of prescriptions in the class for PCPs followed by PAs, followed by NPs (e.g., dabigatran: 4.9 % vs. 3.2 % vs. 2.8 %, respectively). Mean time-to-adoption for the newly approved medications was shorter for PCPs compared to NPs and PAs (e.g., dabigatran, 7.3 vs. 8.2 vs. 8.5 months; P all medications <0.001). CONCLUSIONS: PCPs were more likely to prescribe each of the newly approved medications per each measure of drug adoption, regardless of drug novelty. Differences in the rate and speed of drug adoption between PCPs, NPs, and PAs may have important implications for care and overall costs at the population level as NPs and PAs continue taking on a larger role in prescribing.

Comparing Advanced Practice Providers and Physicians as Providers of e-Visits.

BACKGROUND: Although electronic delivery (electronic visits [e-visits]) of healthcare services by advanced practice providers (APPs) is growing, literature defining the roles of different providers and comparing outcomes is lacking. We analyzed two e-visit models at the University of Pittsburgh Medical Center (UPMC) to compare their providers (physicians and APPs) and associated outcomes. MATERIALS AND METHODS: We identified all e-visits for the UPMC AnywhereCare Continuity (physician providers for existing patients) and Convenience (physician and APP providers for Pennsylvania residents) services (n=2,184) using Epic Systems (Verona, WI) MyChart data (November 2013-August 2014). We compared e-visits by service and provider type for patient characteristics, volume, response time, primary diagnoses, and number of prescriptions. We used statistical tests to determine differences in patient characteristics and an ordinary least square linear regression, controlling for patient characteristics, to determine differences in prescribing. RESULTS: Of the completed e-visits (n=1,791), 72.5% were with APPs, and 27.5% were with physicians. APP patients were younger, higher income, and more likely to be unmarried. Sinusitis patients were more likely to use the Continuity service, whereas those with urinary tract or upper respiratory infections were more likely to use the Convenience service. Finally, provider type was significantly associated with prescribing, with APPs prescribing more. CONCLUSIONS: Some demographic variation exists between users of APP versus physician e-visits. Provider response time seems more driven by service policy than provider type. Finally, variation exists between provider types in quantities of prescriptions written. As health systems and policymakers develop protocols and reimbursement strategies for e-visits, these model considerations will be important.

Innovative approaches to interprofessional care at the University of Pittsburgh Medical Center.

The enactment of the Affordable Care Act expands coverage to millions of uninsured Americans and creates a new workforce landscape. Interprofessional Collaborative Practice (ICP) is no longer a choice but a necessity. In this paper, we describe four innovative approaches to interprofessional practice at the University of Pittsburgh Medical Center. These models demonstrate innovative applications of ICP to inpatient and outpatient care, relying on non-physician providers, training programs, and technology to deliver more appropriate care to specific patient groups. We also discuss the ongoing evaluation plans to assess the effects of these interprofessional practices on patient health, quality of care, and healthcare costs. We conclude that successful implementation of interprofessional teams involves more than just a reassignment of tasks, but also depends on structuring the environment and workflow in a way that facilitates team-based care.

SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care.

BACKGROUND: Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. METHODS AND FINDINGS: All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891-0.916]), c = 0.877 [95% CI:0.864-0.890]), and c = 0.869 [95% CI:0.857-0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4-5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751-0.921], 0.815 [95% CI: 0.730-0.900], and 0.794 [95% CI: 0.725-0.864], respectively). CONCLUSIONS: The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.

A Multisite Case Study of Caregiver Advise, Record, Enable Act Implementation.

BACKGROUND AND OBJECTIVES: The Commonwealth of Pennsylvania passed the Caregiver Advise, Record, Enable (CARE) Act on April 20, 2016. We designed a study to explore early implementation at a large, integrated delivery financing system. Our goal was to assess the effects of system-level decisions on unit implementation and the incorporation of the CARE Act's three components into routine care delivery. RESEARCH DESIGN AND METHODS: We conducted a multisite, ethnographic case study at three different hospitals' medical-surgical units. We conducted observations and semi-structured interview to understand the implementation process and the approach to caregiver identification, notification, and education. We used thematic analysis to code interviews and observations and linked findings to the Promoting Action on Research Implementation in Health Services framework. RESULTS: Organizational context and electronic health record capability were instrumental to the CARE Act implementation and integration into workflow. The implementation team used a decentralized strategy and a variety of communication modes, relying on local hospital units to train staff and make the changes. We found that the system facilitated the CARE Act implementation by placing emphasis on the documentation and charting to demonstrate compliance with the legal requirements. DISCUSSION AND IMPLICATIONS: General acute hospitals will be making or have made similar decisions on how to operationalize the regulatory components and demonstrate compliance with the CARE Act. This study can help to inform others as they design and improve their compliance and implementation strategies.

University of Pittsburgh Medical Center Home Transitions Multidisciplinary Care Coordination Reduces Readmissions for Older Adults.

OBJECTIVES: To compare rates of 30- and 90-day hospital readmissions and observation or emergency department (ED) returns of older adults using the University of Pittsburgh Medical Center (UPMC) Health Plan Home Transitions (HT) with those of Medicare fee-for-service (FFS) controls without HT. DESIGN: Retrospective cohort study. SETTING: Analysis of home health and hospital records from 8 UPMC hospitals in Allegheny County, Pennsylvania, from July 1, 2015, to April 30, 2017. PARTICIPANTS: HT program participants (n=1,900) and controls (n=1,300). INTERVENTION: HT is a care transitions program aimed at preventing readmission that identifies older adults at risk of readmission using a robust inclusion algorithm; deploys a multidisciplinary care team, including a nurse practitioner (NP), a social worker (SW), or both; and provides a multimodal service including personalized care planning, education, treatment, monitoring, and communication facilitation. MEASUREMENT: We used multivariable logistic regression to determine the effects of HT on the odds of hospital readmission and observation or ED return, controlling for index admission participant characteristics and home health process measures. RESULTS: The adjusted odds of 30-day readmission was 0.31 (95% confidence interval (CI) = 0.11-0.87, P = .03) and of 90-day readmission was 0.47 (95% CI=CI = 0.26-0.85, P = .01), for participants at medium risk of readmission in HT who received a team visit. The adjusted odds of 30-day readmission was 0.29 (95% CI = 0.10-0.83, P = .02) for participants at high risk of readmission in HT who received a team visit. The adjusted odds of 30-day observation or ED return was 1.90 (95% CI = 1.28-2.82, P = .001) for participants at medium risk of readmission in HT who received a team visit. CONCLUSION: The HT program may be associated with lower odds of 30- and 90-day hospital readmission and counterbalancing higher odds of observation or ED return. J Am Geriatr Soc 67:156-163, 2019.