Precision health: Determining the capacity of practicing nurses across the United States.

INTRODUCTION: Precision Health (PH) holds the promise of revolutionizing healthcare by enabling personalized disease prevention and management through the integration of genomic data, lifestyle factors, environmental influences, and other social determinants of health (SDoH). However, the absence of a baseline assessment of knowledge, skills, and attitudes (KSAs) of practicing nurses' capacity for PH hinders its integration. The purpose of this study is to determine the capacity of practicing Registered Nurses (RNs) for PH across the United States and to assess the validity and reliability of a tool designed for this use-the Precision Health Nurse Capacity Scale (PHNCS). DESIGN/METHOD: A descriptive exploratory study was conducted to evaluate the capacity of practicing RNs for this evolving phenomenon, PH, using a convenience sample. The survey was sent via email and made available to all members of the American Nurses Association (ANA) who work in a variety of practice environments. The ANA represents the over 4 million nurses practicing in the United States. RESULTS: The majority of nurse respondents felt it is important for nurses to become more educated about all aspects of PH including SDoH but they lack confidence in the integration of PH. The PHNCS was found to be a valid and reliable tool in measuring the capacity of nurses to practice PH. CONCLUSION: The incorporation of PH into nursing practice suffers an immediate impediment: the lack of know-how of the US nursing workforce. This inaugural data on KSAs for PH establishes a logical baseline from which the requisite education and training should commence. CLINICAL RELEVANCE: Precision Health is an emerging healthcare approach in the United States and globally. Enabling it will require a nursing workforce prepared with the requisite KSAs. Determining the capacity of the nursing workforce is a foundational step to begin this process.

Impact of integrating genomic data into the electronic health record on genetics care delivery.

PURPOSE: Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine. METHODS: The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery. RESULTS: The PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P < .001) and managing (1 vs 5 minutes, P < .001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success. CONCLUSION: The PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.

Nurses' role in addressing social determinants of health.

ABSTRACT: Nurses have a vital role in addressing social and health inequities to promote quality healthcare for all. This article discusses the tools to screen for social determinants of health (SDOH) and key considerations for nurses and nurse leaders to advance the integration of SDOH information into their workflows.

Health Information Exchange: Practical Overview and Implications for Nursing Practice.

The US healthcare system is moving into a new era of value-based care, which focuses on delivering safer and higher quality care while reducing costs. Health information exchange (HIE) has been a vital component in this process; however, there has been a lack of awareness and use of HIE among nurse leaders, clinicians, and researchers. The purpose of this article is to provide nurses and administrators with a brief overview of HIE and its impact on care delivery, as well as practical applications using specific case examples.

Impact of a Prescription Drug Monitoring Program on Health Information Exchange Utilization, Prescribing Behaviors, and Care Coordination in an Emergency Department.

Timely access to patient data is critical in patient care. The utilization of health information exchange and prescription drug monitoring programs can make pertinent data readily accessible for emergency department providers to coordinate care. A quasi-experimental preintervention-postintervention design, with 62 providers and 53 554 emergency department visits linked to a health information exchange and prescription drug monitoring program, was used to evaluate rates for utilization, laboratory/imaging orders, narcotic prescribing and readmission. Health information exchange utilization increased significantly after the drug monitoring program was implemented (mean = 119.33 to mean = 231.33, t2 = -15.79, P < .001). There was no significant effect postprescription drug monitoring program for laboratory/imaging orders or narcotics at discharge, although narcotic orders during emergency visits increased (F1,23 = 7.953, P = .010), which may suggest the data confirmed the immediate need to control acute or chronic conditions. In addition, readmission rates decreased from 14.64% to 12.58%. Through streamlining processes, health information exchange and prescription drug monitoring program usage were increased, which can improve care. As organizations promote interoperability of health information, the nurse informaticist plays a significant role in managing access to systems that can assist all providers in coordinating care.

The impact of automatic physician review of prescription drug monitoring program data on opioid prescribing in a Maryland statewide hospital system.

BACKGROUND AND AIMS: Prescription drug monitoring programs (PDMPs) were implemented to decrease dangerous opioid prescribing but have had variable results. This report details how automatic PDMP review changed opioid prescribing across a statewide medical system. DESIGN: An observational study. SETTING: Fourteen hospital networks in the United States. CASES: Healthcare encountered from July 1, 2016 to June 30, 2019. INTERVENTION: Starting from July 1, 2018, the patient's PDMP data would be displayed automatically to providers in the unified electronic medical record (EMR) whenever the provider began to write for an opioid prescription. MEASUREMENTS: Outcomes were prescriptions per encounter (PPE) and the morphine milligram equivalents (MME) per prescription. Outcomes were stratified by practice location, medication prescribed, and diagnosis. All data, including whether the prompt was triggered for a given encounter and whether a prescription was given, were extracted from the EMR. An interrupted timeseries analysis was used to determine how PPE and MME changed in response to the implementation of automatic PDMP review. FINDINGS: Of the 624,488 encounters examined, 18.37 percent (n = 114,710) were in emergency departments, 56.79 percent were admissions (n = 354,634), and 24.84 percent (n = 155,144) were outpatient visits. Opioid prescriptions were started and then canceled 24 percent of the time after the PDMP was shown. There was a decline in MME (ßOverall + Policy Trends = -3.17, p = <0.0001), which was driven by inpatient (ßOverall + Policy Trends = -2.10, p < 0.0001) and outpatient providers (ßOverall + Policy Trends = -3.24, p < 0.01). A decline in MME was seen in all medication categories (-1.72 < ßOverall + Policy Trends < -5.87, p < 0.01). There were no changes in these trends after excluding encounters for severe and acute pain. CONCLUSIONS: Automated PDMP review is associated with fewer prescriptions and smaller doses without decreasing appropriate use.