Pandemic Telemedicine Adoption Trends in a Predominantly Rural Integrated Health System.

Between March 2020 and February 2022, use of telemedicine services in the U.S. shifted dramatically in response to the evolving SARS-CoV2 pandemic. The initial wave caused many non-emergent clinical services to be postponed, including specialty care clinic visits, which were rapidly converted to telemedicine encounters. Telemedicine use ebbed and flowed with subsequent pandemic waves. This paper describes trends in telemedicine use from March 2020-February 2022 at Geisinger, a predominantly rural integrated health system. It highlights characteristics of 5,390 virtual vs. 15,740 in-person clinic visits to neurosurgery and gastroenterology specialists in December 2021 and January 2022. Differences in ordering of diagnostic testing and prescription medications, as well as post-clinic-visit utilization, varied by specialty. Virtual visits in these specialties saved patients from traveling over 174,700 miles/month to attend appointments. Analyzing telemedicine use patterns can inform future resource allocation and determine when virtual encounters can complement or replace in-person specialty care visits.

Overcoming technical and cultural challenges to delivering equitable care for LGBTQ+ individuals in a rural, underserved area.

The lesbian, gay, bisexual, transgender, queer, or questioning (LGBTQ+) community is vulnerable to health-care disparities. Many health-care organizations are working to collect sexual orientation and gender identity in their electronic health records (EHRs), with the goal of providing more inclusive care to their LGBTQ+ patients. There are significant human and technical barriers to making these efforts successful. Based on our 5-year experience at Geisinger (an integrated health system located in a rural, generally conservative area), this case report provides insights to overcome challenges in 4 critical areas: (1) enabling the EHR to collect and use information to support the health-care needs of LGBTQ+ patients, (2) building a culture of awareness and caring, empowering members of the health-care team to break down barriers of misunderstanding and mistrust, (3) developing services to support the needs of LGBTQ+ patients, and (4) partnering with local communities to become a trusted health-care provider.

Bringing Cultural Competency to the EHR: Lessons Learned Providing Respectful, Quality Care to the LGBTQ Community.

The lesbian, gay, bisexual, transgender, queer (LGBTQ) community is vulnerable to healthcare disparities. Many healthcare organizations are contemplating efforts to collect sexual orientation and gender identity in the electronic health record (EHR), with a goal of providing more respectful, inclusive, high-quality care to their LGBTQ patients. There are significant human and technical barriers that must be overcome to make these efforts successful. Based on our four-year experience at Geisinger (an integrated health system located in a rural, generally conservative area), we provide insights to overcome challenges in two critical areas: 1) enabling the EHR to collect and use information to support the healthcare needs of LGBTQ patients, and 2) building a culture of awareness and caring, empowering members of the healthcare team to break down barriers of misunderstanding and mistrust.

Risk of Wrong-Patient Orders Among Multiple vs Singleton Births in the Neonatal Intensive Care Units of 2 Integrated Health Care Systems.

IMPORTANCE: Multiple-birth infants in neonatal intensive care units (NICUs) have nearly identical patient identifiers and may be at greater risk of wrong-patient order errors compared with singleton-birth infants. OBJECTIVES: To assess the risk of wrong-patient orders among multiple-birth infants and singletons receiving care in the NICU and to examine the proportion of wrong-patient orders between multiple-birth infants and siblings (intrafamilial errors) and between multiple-birth infants and nonsiblings (extrafamilial errors). DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study was conducted in 6 NICUs of 2 large, integrated health care systems in New York City that used distinct temporary names for newborns per the requirements of The Joint Commission. Data were collected from 4 NICUs at New York-Presbyterian Hospital from January 1, 2012, to December 31, 2015, and 2 NICUs at Montefiore Health System from July 1, 2013, to June 30, 2015. Data were analyzed from May 1, 2017, to December 31, 2017. All infants in the 6 NICUs for whom electronic orders were placed during the study periods were included. MAIN OUTCOMES AND MEASURES: Wrong-patient electronic orders were identified using the Wrong-Patient Retract-and-Reorder (RAR) Measure. This measure was used to detect RAR events, which are defined as 1 or more orders placed for a patient that are retracted (ie, canceled) by the same clinician within 10 minutes, then reordered by the same clinician for a different patient within the next 10 minutes. RESULTS: A total of 10 819 infants were included: 85.5% were singleton-birth infants and 14.5% were multiple-birth infants (male, 55.8%; female, 44.2%). The overall wrong-patient order rate was significantly higher among multiple-birth infants than among singleton-birth infants (66.0 vs 41.7 RAR events per 100 000 orders, respectively; adjusted odds ratio, 1.75; 95% CI, 1.39-2.20; P < .001). The rate of extrafamilial RAR events among multiple-birth infants (36.1 per 100 000 orders) was similar to that of singleton-birth infants (41.7 per 100 000 orders). The excess risk among multiple-birth infants (29.9 per 100 000 orders) appears to be owing to intrafamilial RAR events. The risk increased as the number of siblings receiving care in the NICU increased; a wrong-patient order error occurred in 1 in 7 sets of twin births and in 1 in 3 sets of higher-order multiple births. CONCLUSIONS AND RELEVANCE: This study suggests that multiple-birth status in the NICU is associated with significantly increased risk of wrong-patient orders compared with singleton-birth status. This excess risk appears to be owing to misidentification between siblings. These results suggest that a distinct naming convention as required by The Joint Commission may provide insufficient protection against identification errors among multiple-birth infants. Strategies to reduce this risk include using given names at birth, changing from temporary to given names when available, and encouraging parents to select names for multiple births before they are born when acceptable to families.

Effect of Restriction of the Number of Concurrently Open Records in an Electronic Health Record on Wrong-Patient Order Errors: A Randomized Clinical Trial.

Importance: Recommendations in the United States suggest limiting the number of patient records displayed in an electronic health record (EHR) to 1 at a time, although little evidence supports this recommendation. Objective: To assess the risk of wrong-patient orders in an EHR configuration limiting clinicians to 1 record vs allowing up to 4 records opened concurrently. Design, Setting, and Participants: This randomized clinical trial included 3356 clinicians at a large health system in New York and was conducted from October 2015 to April 2017 in emergency department, inpatient, and outpatient settings. Interventions: Clinicians were randomly assigned in a 1:1 ratio to an EHR configuration limiting to 1 patient record open at a time (restricted; n = 1669) or allowing up to 4 records open concurrently (unrestricted; n = 1687). Main Outcomes and Measures: The unit of analysis was the order session, a series of orders placed by a clinician for a single patient. The primary outcome was order sessions that included 1 or more wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder measure (an electronic query that identifies orders placed for a patient, retracted, and then reordered shortly thereafter by the same clinician for a different patient). Results: Among the 3356 clinicians who were randomized (mean [SD] age, 43.1 [12.5] years; mean [SD] experience at study site, 6.5 [6.0] years; 1894 females [56.4%]), all provided order data and were included in the analysis. The study included 12 140 298 orders, in 4 486 631 order sessions, placed for 543 490 patients. There was no significant difference in wrong-patient order sessions per 100 000 in the restricted vs unrestricted group, respectively, overall (90.7 vs 88.0; odds ratio [OR], 1.03 [95% CI, 0.90-1.20]; P = .60) or in any setting (ED: 157.8 vs 161.3, OR, 1.00 [95% CI, 0.83-1.20], P = .96; inpatient: 185.6 vs 185.1, OR, 0.99 [95% CI, 0.89-1.11]; P = .86; or outpatient: 7.9 vs 8.2, OR, 0.94 [95% CI, 0.70-1.28], P = .71). The effect did not differ among settings (P for interaction = .99). In the unrestricted group overall, 66.2% of the order sessions were completed with 1 record open, including 34.5% of ED, 53.7% of inpatient, and 83.4% of outpatient order sessions. Conclusions and Relevance: A strategy that limited clinicians to 1 EHR patient record open compared with a strategy that allowed up to 4 records open concurrently did not reduce the proportion of wrong-patient order errors. However, clinicians in the unrestricted group placed most orders with a single record open, limiting the power of the study to determine whether reducing the number of records open when placing orders reduces the risk of wrong-patient order errors. Trial Registration: clinicaltrials.gov Identifier: NCT02876588.

Policies for patient access to clinical data via PHRs: current state and recommendations.

OBJECTIVE: Healthcare delivery organizations are increasingly using online personal health records (PHRs) to provide patients with direct access to their clinical information; however, there may be a lack of consistency in the data made available. We aimed to understand the general use and functionality of PHRs and the organizational policies and decision-making structures for making data available to patients. MATERIALS AND METHODS: A cross-sectional survey was administered by telephone structured interview to 21 organizations to determine the types of data made available to patients through PHRs and the presence of explicit governance for PHR data release. Organizations were identified based on a review of the literature, PHR experts, and snowball sampling. Organizations that did not provide patients with electronic access to their data via a PHR were excluded. RESULTS: Interviews were conducted with 17 organizations for a response rate of 81%. Half of the organizations had explicit governance in the form of a written policy that outlined the data types made available to patients. Overall, 88% of the organizations used a committee structure for the decision-making process and included senior management and information services. All organizations sought input from clinicians. Discussion There was considerable variability in the types of clinical data and the time frame for releasing these data to patients. Variability in data release policies may have implications for PHR use and adoption. CONCLUSIONS: Future policy activities, such as requirement specification for the latter stages of Meaningful Use, should be leveraged as an opportunity to encourage standardization of functionality and broad deployment of PHRs.