Search

OBJECTIVE: To compare the functional capabilities being offered by commercial ambulatory electronic prescribing systems with a set of expert panel recommendations. DESIGN: A descriptive field study of ten commercially available ambulatory electronic prescribing systems, each of which had established a significant market presence. Data were collected from vendors by telephone interview and at sites where the systems were functioning through direct observation of the systems and through personal interviews with prescribers and technical staff. MEASUREMENTS: The capabilities of electronic prescribing systems were compared with 60 expert panel recommendations for capabilities that would improve patient safety, health outcomes, or patients' costs. Each recommended capability was judged as having been implemented fully, partially, or not at all by each system to which the recommendation applied. Vendors' claims about capabilities were compared with the capabilities found in the site visits. RESULTS: On average, the systems fully implemented 50% of the recommended capabilities, with individual systems ranging from 26% to 64% implementation. Only 15% of the recommended capabilities were not implemented by any system. Prescribing systems that were part of electronic health records (EHRs) tended to implement more recommendations. Vendors' claims about their systems' capabilities had a 96% sensitivity and a 72% specificity when site visit findings were considered the gold standard. CONCLUSIONS: The commercial electronic prescribing marketplace may not be selecting for capabilities that would most benefit patients. Electronic prescribing standards should include minimal functional capabilities, and certification of adherence to standards may need to take place where systems are installed and operating. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Electronic prescribing (e-prescribing) may substantially improve health care quality and efficiency, but the available systems are complex and their heterogeneity makes comparing and evaluating them a challenge. The authors aimed to develop a conceptual framework for anticipating the effects of alternative designs for outpatient e-prescribing systems. DESIGN: Based on a literature review and on telephone interviews with e-prescribing vendors, the authors identified distinct e-prescribing functional capabilities and developed a conceptual framework for evaluating e-prescribing systems' potential effects based on their capabilities. Analyses of two commercial e-prescribing systems are presented as examples of applying the conceptual framework. MEASUREMENTS: Major e-prescribing functional capabilities identified and the availability of evidence to support their specific effects. RESULTS: The proposed framework for evaluating e-prescribing systems is organized using a process model of medication management. Fourteen e-prescribing functional capabilities are identified within the model. Evidence is identified to support eight specific effects for six of the functional capabilities. The evidence also shows that a functional capability with generally positive effects can be implemented in a way that creates unintended hazards. Applying the framework involves identifying an e-prescribing system's functional capabilities within the process model and then assessing the effects that could be expected from each capability in the proposed clinical environment. CONCLUSION: The proposed conceptual framework supports the integration of available evidence in considering the full range of effects from e-prescribing design alternatives. More research is needed into the effects of specific e-prescribing functional alternatives. Until more is known, e-prescribing initiatives should include provisions to monitor for unintended hazards. [ABSTRACT FROM AUTHOR]

STUDY OBJECTIVE: The US government provides financial incentives for 'meaningful use' of health information technology, including computerized provider order entry. We assess prevalence of emergency department (ED) computerized provider order entry in 4 states, identify characteristics predicting computerized provider order entry adoption, and assess adoption in 1 state over time, all before incentive programs. METHODS: We surveyed all nonfederal EDs in Massachusetts, Colorado, Georgia, and Oregon, assessing health information technology prevalence in 2008, focusing on computerized provider order entry, an enabler of other health information technology and a key element in itself. We use multivariable logistic regression to evaluate predictors of adoption. We compared the Massachusetts data with data from a similar survey we conducted for Massachusetts in 2005, using 95% confidence intervals (CIs) to assess the change in rate. RESULTS: We identified and surveyed 351 EDs, and 290 (83%) responded to the computerized provider order entry module. Of these, 30% had adopted computerized provider order entry. Odds of computerized provider order entry in rural EDs were 0.07 relative to urban (95% CI 0.01 to 0.39). Oregon EDs had a higher likelihood of computerized provider order entry adoption than Georgia EDs, the state with the lowest adoption (odds ratio 2.9; 95% CI 1.2 to 7.3). In 2005, 15% of Massachusetts EDs reported computerized provider order entry versus 44% in 2008 (29% difference; 95% CI 26% to 32%). CONCLUSION: Health information technology adoption varies by state and urbanicity, with less computerized provider order entry in rural EDs. ED computerized provider order entry adoption nearly tripled in Massachusetts from 2005 to 2008, before any financial inducements. Federal resources might be more effective if they helped providers select health information technology tools, improve health information technology design, and evaluate its influence on care delivery, versus simply calling for 'more'. [ABSTRACT FROM AUTHOR]

Competing Interests: JPS consults for Abbott, Biotronik, Boston Scientific, Cardiologs, Carelog, CVRx, DocGo, EBR, Impulse Dynamics, Implicity, I-Rhythm, Medtronic, Medscape, Microport, Notal Vision, Orchestra BioMed, Oura, Phillips, Sanofi, Smart Cardia, and Vektor Medical. ABL consults for Abbott. VP declares no competing interests.

Background: Electronic paper (E-paper) screens use electrophoretic ink to provide paper-like low-power displays with advanced networking capabilities that may potentially serve as an alternative to traditional whiteboards and television display screens in hospital settings. E-paper may be leveraged in the emergency department (ED) to facilitate communication. Providing ED patient status updates on E-paper screens could improve patient satisfaction and overall experience and provide more equitable access to their health information., Objective: We aimed to pilot a patient-facing digital whiteboard using E-paper to display relevant orienting and clinical information in real time to ED patients. We also sought to assess patients' satisfaction after our intervention and understand our patients' overall perception of the impact of the digital whiteboards on their stay., Methods: We deployed a 41-inch E-paper digital whiteboard in 4 rooms in an urban, tertiary care, and academic ED and enrolled 110 patients to understand and evaluate their experience. Participants completed a modified Hospital Consumer Assessment of Health Care Provider and Systems satisfaction questionnaire about their ED stay. We compared responses to a matched control group of patients triaged to ED rooms without digital whiteboards. We designed the digital whiteboard based on iterative feedback from various departmental stakeholders. After establishing IT infrastructure to support the project, we enrolled patients on a convenience basis into a control and an intervention (digital whiteboard) group. Enrollees were given a baseline survey to evaluate their comfort with technology and an exit survey to evaluate their opinions of the digital whiteboard and overall ED satisfaction. Statistical analysis was performed to compare baseline characteristics as well as satisfaction., Results: After the successful prototyping and implementation of 4 digital whiteboards, we screened 471 patients for inclusion. We enrolled 110 patients, and 50 patients in each group (control and intervention) completed the study protocol. Age, gender, and racial and ethnic composition were similar between groups. We saw significant increases in satisfaction on postvisit surveys when patients were asked about communication regarding delays (P=.03) and what to do after discharge (P=.02). We found that patients in the intervention group were more likely to recommend the facility to family and friends (P=.04). Additionally, 96% (48/50) stated that they preferred a room with a digital whiteboard, and 70% (35/50) found the intervention "quite a bit" or "extremely" helpful in understanding their ED stay., Conclusions: Digital whiteboards are a feasible and acceptable method of displaying patient-facing data in the ED. Our pilot suggested that E-paper screens coupled with relevant, real-time clinical data and packaged together as a digital whiteboard may positively impact patient satisfaction and the perception of the facility during ED visits. Further study is needed to fully understand the impact on patient satisfaction and experience., Trial Registration: ClinicalTrials.gov NCT04497922; https://clinicaltrials.gov/ct2/show/NCT04497922., (©Andrew D A Marshall, Mohammad Adrian Hasdianda, Steven Miyawaki, Guruprasad D Jambaulikar, Chenze Cao, Paul Chen, Christopher W Baugh, Haipeng Zhang, Jonathan McCabe, Lee Steinbach, Scott King, Jason Friedman, Jennifer Su, Adam B Landman, Peter Ray Chai. Originally published in JMIR Formative Research (https://formative.jmir.org), 21.03.2023.)

Objectives: Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital., Design: Single-site, observational cohort study., Setting: An urban, academic hospital in Boston, Massachusetts, USA., Participants: We enrolled adult hospital staff entering the hospital at a key ingress point., Interventions: Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence., Outcome Measures: Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm., Results: One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important., Competing Interests: Competing interests: GT has a financial interest in Teal Bio, a biotechnology company making transparent and reusable respirators. ABL is a member of the Abbot Medical Device Cybersecurity Council. Complete details of all relationships for profit and not for profit for G.T. can be found at the following link: https://www.dropbox.com/sh/szi7vnr4a2ajb56/AABs5N5i0q9AfT1IqIJAET5a?dl=0. No other authors have competing interests to report., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To investigate the effectiveness of a daily attestation system used by employees of a multi-institutional academic medical center, which comprised of symptom-screening, self-referrals to the Occupational Health Services team, and/or a severe acute respiratory coronavirus virus 2 (SARS-CoV-2) test., Design: We conducted a retrospective cohort study of all employee attestations and SARS-CoV-2 tests performed between March and June 2020., Setting: A large multi-institutional academic medical center, including both inpatient and ambulatory settings., Participants: All employees who worked at the study site., Methods: Data were combined from the attestation system (COVIDPass), the employee database, and the electronic health records and were analyzed using descriptive statistics including χ 2 , Wilcoxon, and Kruskal-Wallis tests. We investigated whether an association existed between symptomatic attestations by the employees and the employee testing positive for SARS-CoV-2., Results: After data linkage and cleaning, there were 2,117,298 attestations submitted by 65,422 employees between March and June 2020. Most attestations were asymptomatic (99.9%). The most commonly reported symptoms were sore throat (n = 910), runny nose (n = 637), and cough (n = 570). Among the 2,026 employees who ever attested that they were symptomatic, 905 employees were tested within 14 days of a symptomatic attestation, and 114 (13%) of these tests were positive. The most common symptoms associated with a positive SARS-CoV-2 test were anosmia (23% vs 4%) and fever (46% vs 19%)., Conclusions: Daily symptom attestations among healthcare workers identified a handful of employees with COVID-19. Although the number of positive tests was low, attestations may help keep unwell employees off campus to prevent transmissions.

Objective: Many patients with rheumatoid arthritis (RA) have difficulty finding clinicians to treat them because of workforce shortages. We developed an app to address this problem by improving care efficiency. The app collects patient-reported outcomes (PROs) and can be used to inform visit timing, potentially reducing the volume of low-value visits. We describe the development process, intervention design, and planned study for testing the app., Methods: We employed user-centered design, interviewing patients and clinicians, to develop the app. To improve visit efficiency, symptom tracking logic alerts clinicians to PRO trends: worsening PROs generate alerts suggesting an earlier visit, and stable or improving PROs generate notifications that scheduled visits could be delayed. An interrupted time-series analysis with a nonrandomized control population will allow assessment of the impact of the app on visit frequency., Results: Patient interviews identified several of the following needs for effective app and intervention design: the importance of a simple user interface facilitating rapid answering of PROs, the availability of condensed summary information with links to more in-depth answers to common questions regarding RA, and the need for clinicians to discuss the PRO data during visits with patients. Clinician interviews identified the following user needs: PRO data must be easy to view and use during the clinical workflow, and there should be reduced interval visits when PROs are trending worse. Some clinicians believed visits could be delayed for patients with stable PROs, whereas others raised concerns., Conclusion: PRO apps may improve care efficiency in rheumatology. Formal evaluation of an integrated PRO RA app is forthcoming., (© 2022 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Objective: To describe the incidence of systemic overlap and typical coronavirus disease 2019 (COVID-19) symptoms in healthcare personnel (HCP) following COVID-19 vaccination and association of reported symptoms with diagnosis of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in the context of public health recommendations regarding work exclusion., Design: This prospective cohort study was conducted between December 16, 2020, and March 14, 2021, with HCP who had received at least 1 dose of either the Pfizer-BioNTech or Moderna COVID-19 vaccine., Setting: Large healthcare system in New England., Interventions: HCP were prompted to complete a symptom survey for 3 days after each vaccination. Reported symptoms generated automated guidance regarding symptom management, SARS-CoV-2 testing requirements, and work restrictions. Overlap symptoms (ie, fever, fatigue, myalgias, arthralgias, or headache) were categorized as either lower or higher severity. Typical COVID-19 symptoms included sore throat, cough, nasal congestion or rhinorrhea, shortness of breath, ageusia and anosmia., Results: Among 64,187 HCP, a postvaccination electronic survey had response rates of 83% after dose 1 and 77% after dose 2. Report of ≥3 lower-severity overlap symptoms, ≥1 higher-severity overlap symptoms, or at least 1 typical COVID-19 symptom after dose 1 was associated with increased likelihood of testing positive. HCP with prior COVID-19 infection were significantly more likely to report severe overlap symptoms after dose 1., Conclusions: Reported overlap symptoms were common; however, only report of ≥3 low-severity overlap symptoms, at least 1 higher-severity overlap symptom, or any typical COVID-19 symptom were associated with infection. Work-related restrictions for overlap symptoms should be reconsidered.

In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system. Many previously proposed frameworks for digital health implementation are difficult to operationalize in these dynamic organizations. In this piece, we put forth nine dimensions along which clinically validated digital health tools should be examined by health systems prior to adoption, and propose strategies for selecting digital health tools and planning for implementation in this setting. By evaluating prospective tools along these dimensions, health systems can evaluate which existing digital health solutions are worthy of adoption, ensure they have sufficient resources for deployment and long-term use, and devise a strategic plan for implementation., (© 2022. The Author(s).)

Purpose: The primary objective of this study is to quantify the use of off-label molecularly targeted therapy and describe the clinical situations in which off-label targeted therapy are used. A key secondary objective is to report the outcomes of patients treated with off-label use of targeted therapy., Patients and Methods: We searched the electronic health record between 2000 and 2020 at our center to characterize the volume, clinical settings, and outcomes associated with off-label use of targeted therapies in different types of solid tumors., Results: Among 46,712 patients who received targeted therapies, we identified 119 instances of off-label use of targeted therapy. Colon cancer was the most common cancer type to receive off-label targeted therapy in 18 patients (15.1%), followed by 13 with non-small-cell lung cancer (10.9%), eight with cholangiocarcinoma (6.7%), and seven with glioblastoma (5.9%). The most frequent molecular rationale for off-label therapy came from a comprehensive next-generation sequencing test (53.7%). The most frequently mutated gene that provided the rationale for targeted therapy was BRAF (20.1%), with BRAF V600E being the most common molecular alteration overall (15.1%). The median duration of off-label targeted therapy was 3.58 months, and the overall survival of treated patients was 7.59 months. There were 37 patients (31.1%) treated for longer than 6 months, 23 patients (19.3%) who survived ≥ 2 years, and 13 patients who were still on therapy as of June 2020., Conclusion: In this large cohort study of patients with solid tumors, off-label use of targeted therapy was uncommon. With that said, a notable proportion of patients had treatment durations ≥ 6 months and survivals of ≥ 2 years., Competing Interests: Vivek A. UpadhyayEmployment: EQRxStock and Other Ownership Interests: EQRxConsulting or Advisory Role: Foundation Medicine Bruce E. JohnsonConsulting or Advisory Role: Novartis, Foundation Medicine, Hengrui Therapeutics, Daiichi Sankyo, Lilly, Checkpoint Therapeutics, G1 Therapeutics, Jazz Pharmaceuticals, GlaxoSmithKline, Boston Pharmaceuticals, Janssen Scientific Affairs, GenentechResearch Funding: NovartisPatents, Royalties, Other Intellectual Property: Dana-Farber Cancer Institute Adam B. LandmanPatents, Royalties, Other Intellectual Property: Inventor of COVID Pass symptom screening platform that has been licensed to for-profit organizations (Inst)Travel, Accommodations, Expenses: AbbottOpen Payments Link: https://openpaymentsdata.cms.gov/physician/1076245 Michael J. HassettResearch Funding: IBMNo other potential conflicts of interest were reported.

Patients have benefitted from increasingly sophisticated diagnostic and therapeutic innovations over the years. However, the design of the physical hospital environment has garnered less attention. This may negatively impact a patient's experience and health. In areas of the hospital, such as the emergency department (ED), patients may spend hours, or even days, in a windowless environment. Studies have highlighted the importance of natural light and imagery, as they are essential in providing important stimuli to regulate circadian rhythm and orientation, and to mitigate the onset of certain medical conditions. In hospital locations where standard windows may be infeasible, the use of a virtual window may simulate the benefits of an actual window. In this pilot study, we assessed patient experience and orientation with virtual windows in the ED. We demonstrated that virtual windows are an acceptable technology that may improve patient experience and orientation.

Problem: The SARS-CoV-2 (COVID-19) pandemic presented numerous challenges to inpatient care, including overtaxed inpatient medicine services, surges in patient censuses, disrupted patient care and educational activities for trainees, underused providers in certain specialties, and personal protective equipment shortages and new requirements for physical distancing. In March 2020, as the COVID-19 surge began, an interdisciplinary group of administrators, providers, and trainees at Brigham and Women's Hospital created an inpatient virtual staffing model called the Virtual Team Rounding Program (VTRP)., Approach: The conceptual framework guiding VTRP development was rapid-cycle innovation. The VTRP was designed iteratively using feedback from residents, physician assistants, attendings, and administrators from March to June 2020. The VTRP trained and deployed a diverse set of providers across specialties as "virtual rounders" to support inpatient teams by joining and participating in rounds via videoconference and completing documentation tasks during and after rounds. The program was rapidly scaled up from March to June 2020., Outcomes: In a survey of inpatient providers at the end of the pilot phase, 10/10 (100%) respondents reported they were getting either "a lot" or "a little" benefit from the VTRP and did not find the addition of the virtual rounder burdensome. During the scaling phase, the program grew to support 24 teams. In a survey at the end of the contraction phase, 117/187 (62.6%) inpatient providers who worked with a virtual rounder felt the rounder saved them time. VTRP leadership collaboratively and iteratively developed best practices for challenges encountered during implementation., Next Steps: Virtual rounding provides a valuable extension of inpatient teams to manage COVID-19 surges. Future work will quantitatively and qualitatively assess the impact of the VTRP on inpatient provider satisfaction and well-being, virtual rounders' experiences, and patient care outcomes., (Copyright © 2021 by the Association of American Medical Colleges.)

Importance: Allergic history in individuals with confirmed anaphylaxis to a messenger RNA (mRNA) COVID-19 vaccine is common. However, the risk factors for allergy symptoms after receiving the vaccine are unknown., Objective: To assess the association between self-reported history of high-risk allergy and self-reported allergic reactions after mRNA COVID-19 vaccination of health care employees., Design, Setting, and Participants: This cohort study obtained demographic, medical, and vaccine administration data of employees of Mass General Brigham from the institutional electronic health record. Employees who received at least 1 dose of an mRNA COVID-19 vaccine between December 14, 2020, and February 1, 2021, and who completed at least 1 postvaccination symptom survey in the 3 days after vaccination were included., Exposures: Self-reported history of high-risk allergy, defined as a previous severe allergic reaction to a vaccine, an injectable medication, or other allergen., Main Outcomes and Measures: The primary outcome was 1 or more self-reported allergic reactions in the first 3 days after dose 1 or dose 2 of an mRNA COVID-19 vaccine. Multivariable log binomial regression was used to assess the association between allergic reactions and high-risk allergy status., Results: A total of 52 998 health care employees (mean [SD] age, 42 [14] years; 38 167 women [72.0%]) were included in the cohort, of whom 51 706 (97.6%) received 2 doses of an mRNA COVID-19 vaccine and 474 (0.9%) reported a history of high-risk allergy. Individuals with vs without a history of high-risk allergy reported more allergic reactions after receiving dose 1 or 2 of the vaccine (11.6% [n = 55] vs 4.7% [n = 2461]). In the adjusted model, a history of high-risk allergy was associated with an increased risk of allergic reactions (adjusted relative risk [aRR], 2.46; 95% CI, 1.92-3.16), with risk being highest for hives (aRR, 3.81; 95% CI, 2.33-6.22) and angioedema (aRR, 4.36; 95% CI, 2.52-7.54)., Conclusions and Relevance: This cohort study found that self-reported history of high-risk allergy was associated with an increased risk of self-reported allergic reactions within 3 days of mRNA COVID-19 vaccination. However, reported allergy symptoms did not impede the completion of the 2-dose vaccine protocol among a cohort of eligible health care employees, supporting the overall safety of mRNA COVID-19 vaccine.

Background: Display signage is ubiquitous and essential in hospitals to serve several clerical, operational, and clinical functions, including displaying notices, providing directions, and presenting clinical information. These functions improve efficiency and patient engagement, reduce errors, and enhance the continuity of care. Over time, signage has evolved from analog approaches such as whiteboards and handwritten notices to digital displays such as liquid crystal displays, light emitting diodes, and, now, electronic ink displays. Electronic ink displays are paper-like displays that are not backlit and show content by aligning microencapsulated color beads in response to an applied electric current. Power is only required to generate content and not to retain it. These displays are very readable, with low eye strain; minimize the emission of blue light; require minimal power; and can be driven by several data sources, ranging from virtual servers to electronic health record systems. These attributes make adapting electronic ink displays to hospitals an ideal use case., Objective: In this paper, we aimed to outline the use of signage and displays in hospitals with a focus on electronic ink displays. We aimed to assess the advantages and limitations of using these displays in hospitals and outline the various public-facing and patient-facing applications of electronic ink displays. Finally, we aimed to discuss the technological considerations and an implementation framework that must be followed when adopting and deploying electronic ink displays., Methods: The public-facing applications of electronic ink displays include signage and way-finders, timetables for shared workspaces, and noticeboards and bulletin boards. The clinical display applications may be smaller form factors such as door signs or bedside cards. The larger, ≥40-inch form factors may be used within patient rooms or at clinical command centers as a digital whiteboard to display general information, patient and clinician information, and care plans. In all these applications, such displays could replace analog whiteboards, noticeboards, and even other digital screens., Results: We are conducting pilot research projects to delineate best use cases and practices in adopting electronic ink displays in clinical settings. This will entail liaising with key stakeholders, gathering objective logistical and feasibility data, and, ultimately, quantifying and describing the effect on clinical care and patient satisfaction., Conclusions: There are several use cases in a clinical setting that may lend themselves perfectly to electronic ink display use. The main considerations to be studied in this adoption are network connectivity, content management, privacy and security robustness, and detailed comparison with existing modalities. Electronic ink displays offer a superior opportunity to future-proof existing practices. There is a need for theoretical considerations and real-world testing to determine if the advantages outweigh the limitations of electronic ink displays., (©Guruprasad D Jambaulikar, Andrew Marshall, Mohammad Adrian Hasdianda, Chenzhe Cao, Paul Chen, Steven Miyawaki, Christopher W Baugh, Haipeng Zhang, Jonathan McCabe, Jennifer Su, Adam B Landman, Peter Ray Chai. Originally published in JMIR Formative Research (https://formative.jmir.org), 04.08.2021.)

Purpose: More than 325,000 mobile health (mhealth) applications (apps) have been developed. We sought to describe the state of oncology-specific apps and to highlight areas of strength and opportunities for future development., Methods: We searched for oncology apps in the Apple iOS and Google Play app stores in January 2020. Apps were classified by English language support, date of last update, downloads, intended audience, intended purpose, and developer type., Results: We identified 794 oncology-specific, English language applications; only 257 (32%) met basic recency standards and were considered evaluable. Of evaluable apps, almost half (47%) were found in the Medical Store Category and the majority were free (88%). The most common intended audience was health care professionals (45%), with 28% being geared toward the general public and 27% being intended for patients. The intended function was education for 36%, clinical decision support for 19.5%, and patient support for 18%. Only 23% of education apps and 40% of clinical decision support apps reported any formal app content review process. Web developers created 61.5% of apps, scientific societies created 10%, and hospitals or health care organizations created just 6%. Of 54 studies that used mobile apps in oncology identified by a recent meta-analysis, only two could be matched to commercially available apps from our study, suggesting a substantial divide between investigation and product dissemination., Conclusion: Relatively few oncology-related apps exist in the commercial marketplace, up-to-date apps are uncommon, and there is a notable absence of key oncology stakeholders in app development. Meaningful development opportunities exist., Competing Interests: Vivek A. UpadhyayEmployment: EQRxStock and Other Ownership Interests: EQRxConsulting or Advisory Role: Foundation Medicine Adam B. LandmanConsulting or Advisory Role: AbbottTravel, Accommodations, Expenses: Abbott Michael J. HassettResearch Funding: IBMNo other potential conflicts of interest were reported.

Background: For implanted devices, an effective postmarket surveillance system does not exist. For medications, the Food and Drug Administration's Sentinel Initiative plays that role, relying mainly on drug codes in insurance claims. Unique device identifiers (UDIs) could play an analogous role for implants, but there is no mandate for providers to include UDIs in claims or for payers to record them. Objections have been raised to incorporating UDIs into claims based on a potential burden on providers., Methods: To assess this purported barrier, we modified information systems at 2 provider-payer dyads to allow for the transmission of UDI data from provider to payer. In addition, to illustrate the potential benefit of including device data in claims, we used our data to compare rates of 90-day adverse events after implantation using the electronic health record (EHR) alone with the EHR plus claims., Results: The software system modifications were modest and performed as designed. Moreover, the level of difficulty of their development and implementation was comparable to that associated with a typical new release of an existing system. In addition, our data demonstrated the ability of claims-based data plus EHR data to reveal a larger percentage of postprocedure adverse events than data from EHRs alone., Conclusions: Modifying information systems to allow for the transmission of UDI data from providers to payers should not impose a substantial burden on either. Implementation of a postmarket surveillance system based on such data in claims will require, however, the development of a system analogous to Sentinel., Competing Interests: The other authors disclose no conflict of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

The COVID-19 pandemic has created unique challenges for the U.S. healthcare system due to the staggering mismatch between healthcare system capacity and patient demand. The healthcare industry has been a relatively slow adopter of digital innovation due to the conventional belief that humans need to be at the center of healthcare delivery tasks. However, in the setting of the COVID-19 pandemic, artificial intelligence (AI) may be used to carry out specific tasks such as pre-hospital triage and enable clinicians to deliver care at scale. Recognizing that the majority of COVID-19 cases are mild and do not require hospitalization, Partners HealthCare (now Mass General Brigham) implemented a digitally-automated pre-hospital triage solution to direct patients to the appropriate care setting before they showed up at the emergency department and clinics, which would otherwise consume resources, expose other patients and staff to potential viral transmission, and further exacerbate supply-and-demand mismatching. Although the use of AI has been well-established in other industries to optimize supply and demand matching, the introduction of AI to perform tasks remotely that were traditionally performed in-person by clinical staff represents a significant milestone in healthcare operations strategy., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Importance: Wrong-patient order entry (WPOE) errors have a high potential for harm; these errors are particularly frequent wherever workflows are complex and multitasking and interruptions are common, such as in the emergency department (ED). Previous research shows that interruptive solutions, such as electronic patient verification forms or alerts, can reduce these types of errors but may be time-consuming and cause alert fatigue., Objective: To evaluate whether the use of noninterruptive display of patient photographs in the banner of the electronic health record (EHR) is associated with a decreased rate of WPOE errors., Design, Setting, and Participants: In this cohort study, data collected as part of care for patients visiting the ED of a large tertiary academic urban hospital in Boston, Massachusetts, between July 1, 2017, and June 31, 2019, were analyzed., Exposures: In a quality improvement initiative, the ED staff encouraged patients to have their photographs taken by informing them of the intended safety impact., Main Outcomes and Measures: The rate of WPOE errors (measured using the retract-and-reorder method) for orders placed when the patient's photograph was displayed in the banner of the EHR vs the rate for patients without a photograph displayed. The primary analysis focused on orders placed in the ED; a secondary analysis included orders placed in any care setting., Results: A total of 2 558 746 orders were placed for 71 851 unique patients (mean [SD] age, 49.2 [19.1] years; 42 677 (59.4%) female; 55 109 (76.7%) non-Hispanic). The risk of WPOE errors was significantly lower when the patient's photograph was displayed in the EHR (odds ratio, 0.72; 95% CI, 0.57-0.89). After this risk was adjusted for potential confounders using multivariable logistic regression, the effect size remained essentially the same (odds ratio, 0.57; 95% CI, 0.52-0.61). Risk of error was significantly lower in patients with higher acuity levels and among patients whose race was documented as White., Conclusions and Relevance: This cohort study suggests that displaying patient photographs in the EHR provides decision support functionality for enhancing patient identification and reducing WPOE errors while being noninterruptive with minimal risk of alert fatigue. Successful implementation of such a program in an ED setting involves a modest financial investment and requires appropriate engagement of patients and staff.

Context: The COVID-19 pandemic has highlighted the essential role of palliative care to support the delivery of compassionate, goal-concordant patient care. We created the Web-based application, PalliCOVID (https://pallicovid.app/), in April 2020 to provide all clinicians with convenient access to palliative care resources and support. PalliCOVID features evidence-based clinical guidelines, educational content, and institutional protocols related to palliative care for COVID-19 patients. It is a publicly available resource accessible from any mobile device or desktop computer that provides clinicians with access to palliative care guidance across a variety of care settings, including the emergency department, hospital ward, intensive care unit, and primary care practice., Objective: The primary objective of this study was to evaluate usage patterns of PalliCOVID to understand user behavior in relation to this palliative care content platform during the period of the local peak of COVID-19 infection in Massachusetts., Methods: We retrospectively analyzed deidentified usage data collected by Google Analytics from the first day of PalliCOVID's launch on April 7, 2020, until May 1, 2020, the time period that encompassed the local peak of the COVID-19 surge in Massachusetts. User access data were collected and summarized by using Google Analytics software that had been integrated into the PalliCOVID Web application., Results: A total of 2042 users accessed PalliCOVID and viewed 4637 pages from April 7 to May 1, 2020. Users spent an average of 2 minutes and 6 seconds per session. Eighty-one percent of users were first-time visitors, while the remaining 19% were return visitors. Most users accessed PalliCOVID from the United States (87%), with a large proportion of users coming from Boston and the surrounding cities (32% of overall users)., Conclusions: PalliCOVID is one example of a scalable digital health solution that can bring palliative care resources to frontline clinicians. Analysis of PalliCOVID usage patterns has the potential to inform the improvement of the platform to better meet the needs of its user base and guide future dissemination strategies. The quantitative data presented here, although informative about user behavior, should be supplemented with future qualitative research to further define the impact of this tool and extend our ability to deliver clinical care that is compassionate, rational, and well-aligned with patients' values and goals., (Copyright © 2020 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.)

Purpose: The aim of this study was to assess the prevalence of unscheduled radiologic examination orders in an electronic health record, and the proportion of unscheduled orders that are clinically necessary, by modality., Methods: This retrospective study was conducted from January to October 2016 at an academic institution. All unscheduled radiologic examination orders were retrieved for seven modalities (CT, MR, ultrasound, obstetric ultrasound, bone densitometry, mammography, and fluoroscopy). After excluding duplicates, 100 randomly selected orders from each modality were assigned to two physician reviewers who classified their clinical necessity, with 10% overlap. Interannotator agreement was assessed using κ statistics, the percentage of clinically necessary unscheduled orders was compared, and χ 2 analysis was used to assess differences by modality., Results: A total 494,503 radiologic examination orders were placed during the study period. After exclusions, 33,546 unscheduled orders were identified, 7% of all radiologic examination orders. Among 700 reviewed unscheduled orders, agreement was substantial (κ = 0.63). Eighty-seven percent of bone densitometric examinations and sixty-five percent of mammographic studies were considered clinically necessary, primarily for follow-up management. The majority of orders in each modality were clinically necessary, except for CT, obstetric ultrasound, and fluoroscopy (P < .0001)., Conclusions: Large numbers of radiologic examination orders remain unscheduled in the electronic health record. A substantial portion are clinically necessary, representing potential delays in executing documented provider care plans. Clinically unnecessary unscheduled orders may inadvertently be scheduled and performed. Identifying and performing clinically necessary unscheduled radiologic examination orders may help reduce diagnostic errors related to diagnosis and treatment delays and enhance patient safety, while eliminating clinically unnecessary unscheduled orders will help avoid unneeded testing., (Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Aims: Despite widely available risk stratification tools, safe and effective anticoagulant options, and guideline recommendations, anticoagulation for stroke prevention in atrial fibrillation (AF) is underprescribed. We created and evaluated an alert-based computerized decision support (CDS) strategy to increase anticoagulation prescription in hospitalized AF patients at high risk for stroke., Methods and Results: We enrolled 458 patients (CHA2DS2-VASc score ≥1) with AF who were not prescribed anticoagulant therapy and were hospitalized at Brigham and Women's Hospital. Patients were randomly allocated, according to Attending Physician of record, to intervention (alert-based CDS) vs. control (no notification). The primary efficacy outcome was the frequency of anticoagulant prescription. The CDS tool assigned 248 patients to the alert group and 210 to the control group. Patients in the alert group were more likely to be prescribed anticoagulation during the hospitalization (25.8% vs. 9.5%, P < 0.0001), at discharge (23.8% vs. 12.9%, P = 0.003), and at 90 days (27.7% vs. 17.1%, P = 0.007). The alert reduced the odds of a composite outcome of death, myocardial infarction (MI), cerebrovascular event, and systemic embolic event at 90 days [11.3% vs. 21.9%, P = 0.002; odds ratio (OR) 0.45; 95% confidence interval (CI) 0.27-0.76]. The alert reduced the odds of MI at 90 days by 87% (1.2% vs. 8.6%, P = 0.0002; OR 0.13; 95% CI 0.04-0.45) and cerebrovascular events or systemic embolism at 90 days by 88% (0% vs. 2.4%, P = 0.02; OR 0.12; 95% CI 0.0-0.91)., Conclusion: An alert-based CDS strategy increased anticoagulation in high-risk hospitalized AF patients and reduced major adverse cardiovascular events, including MI and stroke., Clinicaltrials.gov Identifier: NCT02339493., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Objectives/hypothesis: Institutional and national programs have endorsed the use of validated instruments in daily practice, but their utility for frontline clinicians is unknown. Our first objective was thus to determine if routinely obtained patient descriptions accurately predict disease-specific validated instrument results. Our second objective was to assess the feasibility of deploying an electronic system to obtain and document patient-reported descriptions of health status., Study Design: Prospective assessment of consecutive eligible ambulatory care patients., Methods: For the first objective, patients presenting with the same chief complaint at consecutive visits provided concurrent routine symptom descriptions and validated instrument responses. To determine whether patients' routine descriptions predicted scores from disease-specific instruments, receiver operator characteristic (ROC) curves, sensitivity/specificity, Spearman ρ correlation, and regression models were utilized. For the second objective, feasibility assessment focused on percent with successful response capture, time to questionnaire completion, and impact on physician time needed to obtain and document patient histories., Results: For the first objective, among patients with hearing loss, eustachian tube dysfunction, nasal obstruction, and chronic rhinosinusitis, the areas under the ROC curves were 0.38 to 0.83. Spearman ρ coefficients were 0.25 to 0.46. Sensitivity and specificity ranged from 15.9% to 87.8%, with correct classification in 42.3% to 55.2% of cases. For the second objective, there was a 91% completion rate. Completion times were 7:18 to 12:37 for new patients and 3:23 to 6:41 for established patients. Physician time savings were estimated at 11 minutes per patient., Conclusions: Using validated instruments and electronic data collection systems in daily practice has practical and clinical implications. These topics warrant further study., Level of Evidence: 2c Laryngoscope, 130:S1-S13, 2020., (© 2019 The American Laryngological, Rhinological and Otological Society, Inc.)

Objective: Clinical Informatics is a large part of the current surgical practice, yet, there is no formal curriculum to teach residents about informatics. The purpose of this article is to describe the field of clinical informatics, describe the potential of informatics to improve surgical care, and propose a framework to train surgical informaticians., Design: This is a perspective summarizing the role of clinical informatics in current surgical training., Conclusions: The manuscript concisely discusses the potential impact of novel technologies and information systems on surgical care delivery. We demonstrate the importance of informatics training for surgeons and describe the relatively new fellowship program as well as the various pathways to obtain board certification in clinical informatics., (Copyright © 2019 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

Objective: To assess the role of speech recognition (SR) technology in clinicians' documentation workflows by examining use of, experience with and opinions about this technology., Materials and Methods: We distributed a survey in 2016-2017 to 1731 clinician SR users at two large medical centers in Boston, Massachusetts and Aurora, Colorado. The survey asked about demographic and clinical characteristics, SR use and preferences, perceived accuracy, efficiency, and usability of SR, and overall satisfaction. Associations between outcomes (e.g., satisfaction) and factors (e.g., error prevalence) were measured using ordinal logistic regression., Results: Most respondents (65.3%) had used their SR system for under one year. 75.5% of respondents estimated seeing 10 or fewer errors per dictation, but 19.6% estimated half or more of errors were clinically significant. Although 29.4% of respondents did not include SR among their preferred documentation methods, 78.8% were satisfied with SR, and 77.2% agreed that SR improves efficiency. Satisfaction was associated positively with efficiency and negatively with error prevalence and editing time. Respondents were interested in further training about using SR effectively but expressed concerns regarding software reliability, editing and workflow., Discussion: Compared to other documentation methods (e.g., scribes, templates, typing, traditional dictation), SR has emerged as an effective solution, overcoming limitations inherent in other options and potentially improving efficiency while preserving documentation quality., Conclusion: While concerns about SR usability and accuracy persist, clinicians expressed positive opinions about its impact on workflow and efficiency. Faster and better approaches are needed for clinical documentation, and SR is likely to play an important role going forward., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The rarity and heterogeneity of sarcomas make performing appropriately powered studies challenging and magnify the significance of large databases in sarcoma research. Established large tumor registries and population-based databases have become increasingly relevant for answering clinical questions regarding sarcoma incidence, treatment patterns, and outcomes. However, the validity of large databases has been questioned and scrutinized because of the inaccuracy and wide variability of coding practices and the absence of clinically relevant variables. In addition, the utilization of large databases for the study of rare cancers such as sarcoma may be particularly challenging because of the known limitations of administrative data and poor overall data quality. Currently, there are several large national cancer databases, including the Surveillance, Epidemiology, and End Results database, the National Cancer Data Base of the American College of Surgeons and the American Cancer Society, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention. These databases are often used for sarcoma research, but they are limited by their dependence on administrative or billing data, the lack of agreement between chart abstractors on diagnosis codes, and the use of preexisting documented hospital diagnosis codes for tumor registries, which lead to a significant underestimation of sarcomas in large data sets. Current and future initiatives to improve databases and big data applications for sarcoma research include increasing the utilization of sarcoma-specific registries and encouraging national initiatives to expand on real-world, evidence-based data sets., (© 2019 American Cancer Society.)

Objective: Assess whether introducing order priorities with defined performance expectations in the electronic health record (EHR) reduces immediate inpatient radiology orders., Materials and Methods: This Institutional Review Board-approved, retrospective study was performed at a 776-bed academic hospital conducting 164,000+ inpatient radiology examinations annually. Study period was January 2, 2017, to July 23, 2017; 14 weeks pre- and postimplementation of an education-only intervention including replacing urgent and as soon as possible priorities with imaging within next 6, 12, or 24 hours; imaging in the morning; and required for discharge priorities. STAT routine, timed, today order priorities remained unchanged. Institution-wide training immediately pre- and postimplementation was provided through two waves of e-mail and electronic tip sheets. Primary outcome measure was total STAT studies ordered of total radiology studies ordered per week (STAT rate). Secondary outcomes were non-STAT, non-routine (non-SR) order rate, and routine order rate. Paired t test and statistical process control (SPC) analysis were performed., Results: STAT rate pre- (22.5%, 7,150 STAT of 31,765 total; weeks 1-14) and postintervention (23.4%, 7,481 STAT of 32,034 total; weeks 16-29) remained unchanged (P = .37). SPC demonstrated no special cause variation. Postintervention non-SR rate increased 3-fold (2.7%, 859 non-SR of 31,765 total pre-intervention versus 8.2%, 2,615 non-SR of 32,034 total postintervention; 8.2%/2.7% = 3.0; P < .0001). There was an 8.8% relative reduction in routine rate postintervention (73.9%, 23,471 routine of 31,765 total pre-intervention; 67.4%, 21,579 routine of 32,034 total postintervention; (73.9% - 67.4%)/73.9% × 100 = 8.8%; P < .0001)., Conclusion: Implementing ordering priorities with defined performance expectations in the EHR reduced routine but did not reduce STAT inpatient radiology orders. More stringent interventions may be needed to reduce unnecessary STAT inpatient radiology ordering to improve use of limited imaging resources., (Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Objectives: To more clearly define the landscape of digital medical devices subject to US Food and Drug Administration (FDA) oversight, this analysis leverages publicly available regulatory documents to characterise the prevalence and trends of software and cybersecurity features in regulated medical devices., Design: We analysed data from publicly available FDA product summaries to understand the frequency and recent time trends of inclusion of software and cybersecurity content in publicly available product information., Setting: The full set of regulated medical devices, approved over the years 2002-2016 included in the FDA's 510(k) and premarket approval databases., Primary and Secondary Outcome Measures: The primary outcome was the share of devices containing software that included cybersecurity content in their product summaries. Secondary outcomes were differences in these shares (a) over time and (b) across regulatory areas., Results: Among regulated devices, 13.79% were identified as including software. Among these products, only 2.13% had product summaries that included cybersecurity content over the period studied. The overall share of devices including cybersecurity content was higher in recent years, growing from an average of 1.4% in the first decade of our sample to 5.5% in 2015 and 2016, the most recent years included. The share of devices including cybersecurity content also varied across regulatory areas from a low of 0% to a high of 22.2%., Conclusions: To ensure the safest possible healthcare delivery environment for patients and hospitals, regulators and manufacturers should work together to make the software and cybersecurity content of new medical devices more easily accessible., Competing Interests: Competing interests: ABL is a member of the Abbott Medical Device Cybersecurity Council. DK is supported by the Greenwall Faculty Scholars Program in Bioethics, is a consultant to Circulatory Systems Advisory Panel of the Food and Drug Administration, and has provided consulting to the Baim Institute for Clinical Research for clinical trials of medical devices (unrelated to the study topic)., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Restroom cleanliness is an important factor in hospital quality. Due to its dynamic process, it can be difficult to detect the presence of dirty restrooms that need to be cleaned. Using an Internet of Things (IoT) button can permit users to designate restrooms that need cleaning and in turn, allow prompt response from housekeeping to maintain real-time restroom cleanliness., Objective: This study aimed to describe the deployment of an IoT button-based notification system to measure hospital restroom cleanliness reporting system usage and qualitative feedback from housekeeping staff on IoT button use., Methods: We deployed IoT buttons in 16 hospital restrooms. Over an 8-month period, housekeeping staff received real-time notifications and responded to button presses for restroom cleaning. All button presses were recorded. We reported average button usage by hospital area, time of day, and day of week. We also conducted interviews with housekeeping supervisors and staff to understand their acceptance of and experience with the system., Results: Over 8 months, 1920 requests to clean restrooms in the main hospital lobby and satellite buildings were received. The hospital lobby IoT buttons received over half (N=1055, 55%) of requests for cleaning. Most requests occurred in afternoon hours from 3 PM to midnight. Requests for cleaning remained stable throughout the work week with fewer requests occurring over weekends. IoT button use was sustained throughout the study period. Interviews with housekeeping supervisors and staff demonstrated acceptance of the IoT buttons; actual use was centered around asynchronous communication between supervisors and staff in response to requests to clean restrooms., Conclusions: An IoT button system is a feasible method to generate on-demand request for restroom cleaning that is easy to deploy and that users will consistently engage with. Data from this system have the potential to enable responsive scheduling for restroom service and anticipate periods of high restroom utilization in a hospital., (©Peter R Chai, Haipeng Zhang, Guruprasad D Jambaulikar, Edward W Boyer, Labina Shrestha, Loay Kitmitto, Paige G Wickner, Hojjat Salmasian, Adam B Landman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.06.2019.)

Healthcare providers make time-sensitive care decisions based on EHR data. As systems of record, the EHR is often not configured to optimally surface timely information. For patients awaiting admission, infection control concerns that potentially require private rooms can prolong stays in the Emergency Department. We aim to determine if an event-based notification platform connected with a commercial EHR can help prioritize timely information and improve patient flow in the emergency department. We undertook a pre-post analysis for patients being admitted from the emergency room who were tested for influenza. We used a primary outcome of mean time from negative test result to inpatient transfer. The median time decreased by 27%, from 4.1 hours to 3.0 hours. The distribution of transfer times pre and post-intervention were significantly different with a p-value of <0.001. Our findings support the use of event-based notification systems to improve patient flow in the emergency department.

Importance: Cybersecurity is an increasingly important threat to health care delivery, and email phishing is a major attack vector against hospital employees., Objective: To describe the practice of phishing simulation and the extent to which health care employees are vulnerable to phishing simulations., Design, Setting, and Participants: Retrospective, multicenter quality improvement study of a convenience sample of 6 geographically dispersed US health care institutions that ran phishing simulations from August 1, 2011, through April 10, 2018. The specific institutions are anonymized herein for security and privacy concerns., Exposures: Simulated phishing emails received by employees at US health care institutions., Main Outcomes and Measures: Date of phishing campaign, campaign number, number of emails sent, number of emails clicked, and email content. Emails were classified into 3 categories (office related, personal, or information technology related)., Results: The final study sample included 6 anonymized US health care institutions, 95 simulated phishing campaigns, and 2 971 945 emails, 422 062 of which were clicked (14.2%). The median institutional click rates for campaigns ranged from 7.4% (interquartile range [IQR], 5.8%-9.6%) to 30.7% (IQR, 25.2%-34.4%), with an overall median click rate of 16.7% (IQR, 8.3%-24.2%) across all campaigns and institutions. In the regression model, repeated phishing campaigns were associated with decreased odds of clicking on a subsequent phishing email (adjusted OR, 0.511; 95% CI, 0.382-0.685 for 6-10 campaigns; adjusted OR, 0.335; 95% CI, 0.282-0.398 for >10 campaigns)., Conclusions and Relevance: Among a sample of US health care institutions that sent phishing simulations, almost 1 in 7 simulated emails sent were clicked on by employees. Increasing campaigns were associated with decreased odds of clicking on a phishing email, suggesting a potential benefit of phishing simulation and awareness. With cyberattacks increasing against US health care systems, these click rates represent a major cybersecurity risk for hospitals.

We conducted a national study to assess the numbers and diversity of applicants for 2016 and 2017 clinical informatics fellowship positions. In each year, we collected data on the number of applications that programs received from candidates who were ultimately successful vs. unsuccessful. In 2017, we also conducted an anonymous applicant survey. Successful candidates applied to an average of 4.2 and 5.5 programs for 2016 and 2017, respectively. In the survey, unsuccessful candidates reported applying to fewer programs. Assuming unsuccessful candidates submitted between 2-5 applications each, the total applicant pool numbered 42-69 for 2016 (competing for 24 positions) and 52-85 for 2017 (competing for 30 positions). Among survey respondents (n=33), 24% were female, 1 was black and none were Hispanic. We conclude that greater efforts are needed to enhance interest in clinical informatics among medical students and residents, particularly among women and members of underrepresented minority groups.

To facilitate follow-up of critical test results across transitions in patient care settings, we implemented an electronic discharge module that enabled care providers to include follow-up recommendations in the discharge instructions. We assessed the impact of this module on documentation of follow-up recommendations for critical imaging findings in Emergency Department (ED) discharge instructions. We studied 240 patients with critical imaging findings discharged from the ED before (n = 80) and after (n = 160) implementation of the module. We manually reviewed hand-written forms and electronic discharge instructions to determine if follow-up recommendations were documented. Follow-up recommendations in ED discharge instructions increased from 60.0% (48/80) to 73.8% (118/160) post-module implementation (p = 0.03), a relative increase of 23%. There was no significant change in the rate of documented critical imaging findings in the discharge instructions (77.5% [62/80] before the intervention and 76.9% [123/160] after the intervention; p = 0.91). Implementation of a discharge module was associated with increased documentation of critical imaging finding follow-up recommendations in ED discharge instructions. However, one in four patients still did not receive adequate follow-up recommendations, suggesting further opportunities for performance improvement exist.

Importance: Accurate clinical documentation is critical to health care quality and safety. Dictation services supported by speech recognition (SR) technology and professional medical transcriptionists are widely used by US clinicians. However, the quality of SR-assisted documentation has not been thoroughly studied., Objective: To identify and analyze errors at each stage of the SR-assisted dictation process., Design Setting and Participants: This cross-sectional study collected a stratified random sample of 217 notes (83 office notes, 75 discharge summaries, and 59 operative notes) dictated by 144 physicians between January 1 and December 31, 2016, at 2 health care organizations using Dragon Medical 360 | eScription (Nuance). Errors were annotated in the SR engine-generated document (SR), the medical transcriptionist-edited document (MT), and the physician's signed note (SN). Each document was compared with a criterion standard created from the original audio recordings and medical record review., Main Outcomes and Measures: Error rate; mean errors per document; error frequency by general type (eg, deletion), semantic type (eg, medication), and clinical significance; and variations by physician characteristics, note type, and institution., Results: Among the 217 notes, there were 144 unique dictating physicians: 44 female (30.6%) and 10 unknown sex (6.9%). Mean (SD) physician age was 52 (12.5) years (median [range] age, 54 [28-80] years). Among 121 physicians for whom specialty information was available (84.0%), 35 specialties were represented, including 45 surgeons (37.2%), 30 internists (24.8%), and 46 others (38.0%). The error rate in SR notes was 7.4% (ie, 7.4 errors per 100 words). It decreased to 0.4% after transcriptionist review and 0.3% in SNs. Overall, 96.3% of SR notes, 58.1% of MT notes, and 42.4% of SNs contained errors. Deletions were most common (34.7%), then insertions (27.0%). Among errors at the SR, MT, and SN stages, 15.8%, 26.9%, and 25.9%, respectively, involved clinical information, and 5.7%, 8.9%, and 6.4%, respectively, were clinically significant. Discharge summaries had higher mean SR error rates than other types (8.9% vs 6.6%; difference, 2.3%; 95% CI, 1.0%-3.6%; P < .001). Surgeons' SR notes had lower mean error rates than other physicians' (6.0% vs 8.1%; difference, 2.2%; 95% CI, 0.8%-3.5%; P = .002). One institution had a higher mean SR error rate (7.6% vs 6.6%; difference, 1.0%; 95% CI, -0.2% to 2.8%; P = .10) but lower mean MT and SN error rates (0.3% vs 0.7%; difference, -0.3%; 95% CI, -0.63% to -0.04%; P = .03 and 0.2% vs 0.6%; difference, -0.4%; 95% CI, -0.7% to -0.2%; P = .003)., Conclusions and Relevance: Seven in 100 words in SR-generated documents contain errors; many errors involve clinical information. That most errors are corrected before notes are signed demonstrates the importance of manual review, quality assurance, and auditing., Competing Interests: Conflict of Interest Disclosures: Dr Zhou reported grants from the Agency for Healthcare Research and Quality during the conduct of the study Dr Doan reported personal fees from Sanofi Genzyme outside the submitted work. Dr Meteer reported grants from the Agency for Healthcare Research and Quality during the conduct of the study. Dr Bates reported grants from the National Library of Medicine during the conduct of the study; had a patent (No. 6029138) issued, licensed, and with royalties paid; is a coinventor on patent No. 6029138 held by Brigham and Women’s Hospital on the use of decision support software for medical management, licensed to the Medicalis Corporation; holds a minority equity position in the privately held company Medicalis, which develops web-based decision support for radiology test ordering; serves on the board for S.E.A. Medical Systems, which makes intravenous pump technology; consults for EarlySense, which makes patient safety monitoring systems; receives cash compensation from CDI-Negev, which is a nonprofit incubator for health IT startups; receives equity from Valera Health, which makes software to help patients with chronic diseases; receives equity from Intensix, which makes software to support clinical decision making in intensive care; and receives equity from MDClone, which takes clinical data and produces deidentified versions of it. Dr Bates’ financial interests have been reviewed by Brigham and Women’s Hospital and Partners HealthCare in accordance with their institutional policies. No other disclosures were reported.

Despite considerable investment in digital health (DH) companies and a growing DH ecosystem, there are multiple challenges to testing and implementing innovative solutions. Health systems have recognized the potential of DH and have formed DH innovation centers. However, limited information is available on DH innovation center processes, best practices, or outcomes. This case report describes a DH innovation center process that can be replicated across health systems and defines and benchmarks process indicators to assess DH innovation center performance. The Brigham and Women's Hospital's Digital Health Innovation Group (DHIG) accelerates DH innovations from idea to pilot safely and efficiently using a structured process. Fifty-four DH innovations were accelerated by the DHIG process between July 2014 and December 2016. In order to measure effectiveness of the DHIG process, key process indicators were defined as 1) number of solutions that completed each DHIG phase and 2) length of time to complete each phase. Twenty-three DH innovations progressed to pilot stage and 13 innovations were terminated after barriers to pilot implementation were identified by the DHIG process. For 4 DH solutions that executed a pilot, the average time for innovations to proceed from DHIG intake to pilot initiation was 9 months. Overall, the DHIG is a reproducible process that addresses key roadblocks in DH innovation within health systems. To our knowledge, this is the first report to describe DH innovation process indicators and results within an academic health system. Therefore, there is no published data to compare our results with the results of other DH innovation centers. Standardized data collection and indicator reporting could allow benchmark comparisons across institutions. Additional opportunities exist for the validation of DH solution effectiveness and for translational support from pilot to implementation. These are critical steps to advance DH technologies and effectively leverage the DH ecosystem to transform healthcare., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Rheumatoid arthritis flares have a profound effect on patients, causing pain and disability. However, flares often occur between regularly scheduled health care provider visits and are, therefore, difficult to monitor and manage. We sought to develop a mobile phone app combined with a population management system to help track RA flares between visits., Objective: The objective of this study is to implement the mobile app plus the population management system to monitor rheumatoid arthritis disease activity between scheduled health care provider visits over a period of 6 months., Methods: This is a randomized controlled trial that lasts for 6 months for each participant. We aim to recruit 190 patients, randomized 50:50 to the intervention group versus the control group. The intervention group will be assigned the mobile app and be prompted to answer daily questionnaires sent to their mobile devices. Both groups will be assigned a population manager, who will communicate with the participants via telephone at 6 weeks and 18 weeks. The population manager will also communicate with the participants in the intervention group if their responses indicate a sustained increase in rheumatoid arthritis disease activity. To assess patient satisfaction, the primary outcomes will be scores on the Treatment Satisfaction Questionnaire for Medication as well as the Perceived Efficacy in Patient-Physician Interactions questionnaire at 6 months. To determine the effect of the mobile app on rheumatoid arthritis disease activity, the primary outcome will be the Clinical Disease Activity Index at 6 months., Results: The trial started in November 2016, and an estimated 2.5 years will be necessary to complete the study. Study results are expected to be published by the end of 2019., Conclusions: The completion of this study will provide important data regarding the following: (1) the assessment of validated outcome measures to assess rheumatoid arthritis disease activity with a mobile app between routinely scheduled health care provider visits, (2) patient engagement in monitoring their condition, and (3) communication between patients and health care providers through the population management system., Trial Registration: ClinicalTrials.gov NCT02822521, http://clinicaltrials.gov/ct2/show/NCT02822521 (Archived by WebCite at http://www.webcitation.org/6xed3kGPd)., (©Penny Wang, Dee Luo, Fengxin Lu, Josephine S Elias, Adam B Landman, Kaleb D Michaud, Yvonne C Lee. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 11.04.2018.)

Study Objective: Electronic health record implementation can improve care, but may also adversely affect emergency department (ED) efficiency. We examine how a custom, ED provider, electronic documentation system (eDoc), which replaced paper documentation, affects operational performance., Methods: We analyzed retrospective operational data for 1-year periods before and after eDoc implementation in a single ED. We computed daily operational statistics, reflecting 60,870 pre- and 59,337 postimplementation patient encounters. The prespecified primary outcome was daily mean length of stay; secondary outcomes were daily mean length of stay for admitted and discharged patients and daily mean arrival time to disposition for admitted patients. We used a prespecified multiple regression model to identify differences in outcomes while controlling for prespecified confounding variables., Results: The unadjusted change in length of stay was 8.4 minutes; unadjusted changes in secondary outcomes were length of stay for admitted patients 11.4 minutes, length of stay for discharged patients 1.8 minutes, and time to disposition 1.8 minutes. With a prespecified regression analysis to control for variations in operational characteristics, there were significant increases in length of stay (6.3 minutes [95% confidence interval 3.5 to 9.1 minutes]) and length of stay for discharged patients (5.1 minutes [95% confidence interval 1.9 to 8.3 minutes]). There was no statistically significant change in length of stay for admitted patients or time to disposition., Conclusion: In our single-center study, the isolated implementation of eDoc was associated with increases in overall and discharge length of stay. Our findings suggest that a custom-designed electronic provider documentation may negatively affect ED throughput. Strategies to mitigate these effects, such as reducing documentation requirements or adding clinical staff, scribes, or voice recognition, would be a valuable area of future research., (Copyright © 2017 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Background mHealth apps may be useful tools for supporting chronic disease management. Objective Our aim was to apply user-centered design principles to efficiently identify core components for an mHealth-based asthma symptom–monitoring intervention using patient-reported outcomes (PROs). Methods We iteratively combined principles of qualitative research, user-centered design, and “gamification” to understand patients' and providers' needs, develop and refine intervention components, develop prototypes, and create a usable mobile app to integrate with clinical workflows. We identified anticipated benefits and burdens for stakeholders. Results We conducted 19 individual design sessions with nine adult patients and seven clinicians from an academic medical center (some were included multiple times). We identified four core intervention components: (1) Invitation—patients are invited by their physicians. (2) Symptom checks—patients receive weekly five-item questionnaires via the app with 48 hours to respond. Depending on symptoms, patients may be given the option to request a call from a nurse or receive one automatically. (3) Patient review—in the app, patients can view their self-reported data graphically. (4) In-person visit—physicians have access to patient-reported symptoms in the electronic health record (EHR) where they can review them before in-person visits. As there is currently no location in the EHR where physicians would consistently notice these data, recording a recent note was the best option. Benefits to patients may include helping decide when to call their provider and facilitating shared decision making. Benefits to providers may include saving time discussing symptoms. Provider organizations may need to pay nurses extra, but those costs may be offset by reduced visits and hospitalizations. Conclusion Recent systematic reviews show inconsistent outcomes and little insight into functionalities required for mHealth asthma interventions, highlighting the need for systematic intervention design. We identified specific features for adoption and engagement that meet the stated needs of users for asthma symptom monitoring., Competing Interests: Conflict of Interest: Dr. Zimlichman is an advisor to Valera Health. Dr. Landman reports he is a senior editor at Ranked Health, a project that evaluates digital health apps, run by the Hacking Medicine Institute, a nonprofit organization. Dr. Bates is a coinventor on Patent No. 6029138 held by Brigham and Women's Hospital on the use of decision support software for medical management, licensed to the Medicalis Corporation. He holds a minority equity position in the privately held company Medicalis which develops web-based decision support for radiology test ordering. He serves on the board for SEA Medical Systems, which makes intravenous pump technology. He consults for EarlySense, which makes patient safety monitoring systems. He receives equity and cash compensation from QPID, Inc, a company focused on intelligence systems for electronic health records. He receives cash compensation from CDI (Negev), Ltd, which is a not-for-profit incubator for health IT startups. He receives equity from Enelgy which makes software to support evidence-based clinical decisions. He receives equity from Valera Health which makes software to help patients with chronic diseases. He receives equity from Intensix which makes software to support clinical decision making in intensive care. He receives equity from MDClone which takes clinical data and produces de-identified versions of it. Dr. Bates' financial interests have been reviewed by Brigham and Women's Hospital and Partners HealthCare in accordance with their institutional policies.

Background: Recent U.S. government regulations incentivize implementation of an electronic health record (EHR) with computerized order entry and structured results display. Many institutions have also chosen to interface their EHR to their laboratory information system (LIS). Reported long-term benefits include increased efficiency and improved quality and safety. In order to successfully implement an interfaced EHR-LIS, institutions must plan years in advance and anticipate the impact of an integrated system. It can be challenging to fully understand the technical, workflow and resource aspects and adequately prepare for a potentially protracted system implementation and the subsequent stabilization., Objectives: We describe the top ten challenges that we encountered in our clinical laboratories following the implementation of an interfaced EHR-LIS and offer suggestions on how to overcome these challenges., Methods: This study was performed at a 777-bed, tertiary care center which recently implemented an interfaced EHR-LIS. Challenges were recorded during EHR-LIS implementation and stabilization and the authors describe the top ten., Results: Our top ten challenges were selection and harmonization of test codes, detailed training for providers on test ordering, communication with EHR provider champions during the build process, fluid orders and collections, supporting specialized workflows, sufficient reports and metrics, increased volume of inpatient venipunctures, adequate resources during stabilization, unanticipated changes to laboratory workflow and ordering specimens for anatomic pathology. A few suggestions to overcome these challenges include regular meetings with clinical champions, advanced considerations of reports and metrics that will be needed, adequate training of laboratory staff on new workflows in the EHR and defining all tests including anatomic pathology in the LIS., Conclusion: EHR-LIS implementations have many challenges requiring institutions to adapt and develop new infrastructures. This article should be helpful to other institutions facing or undergoing a similar endeavor., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Patient satisfaction in outpatient phlebotomy settings typically depends on wait time and venipuncture experience, and many patients equate their experiences with their overall satisfaction with the hospital., Methods: We compared patient service times and preanalytical errors pre- and postimplementation of an integrated electronic health record (EHR)-laboratory information system (LIS) and electronic specimen collection module. We also measured patient wait time and assessed patient satisfaction using a 5-question survey., Results: The percentage of patients waiting less than 10 minutes increased from 86% preimplementation to 93% postimplementation of the EHR-LIS (P ≤.001). The median total service time decreased significantly, from 6 minutes (IQR, 4-8 minutes), to 5 minutes (IQR, 3-6 minutes) (P = .005). The preanalytical errors decreased significantly, from 3.20 to 1.93 errors per 1000 specimens (P ≤.001). Overall patient satisfaction improved, with an increase in excellent responses for all 5 questions (P ≤.001)., Conclusions: We found several benefits of implementing an electronic specimen collection module, including decreased wait and service times, improved patient satisfaction, and a reduction in preanalytical errors., (© American Society for Clinical Pathology, 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com)

Context: - A recent government regulation incentivizes implementation of an electronic health record (EHR) with computerized order entry and structured results display. Many institutions have also chosen to interface their EHR with their laboratory information system (LIS)., Objective: - To determine the impact of an interfaced EHR-LIS on laboratory processes., Design: - We analyzed several different processes before and after implementation of an interfaced EHR-LIS: the turnaround time, the number of stat specimens received, venipunctures per patient per day, preanalytic errors in phlebotomy, the number of add-on tests using a new electronic process, and the number of wrong test codes ordered. Data were gathered through the LIS and/or EHR., Results: - The turnaround time for potassium and hematocrit decreased significantly (P = .047 and P = .004, respectively). The number of stat orders also decreased significantly, from 40% to 7% for potassium and hematocrit, respectively (P < .001 for both). Even though the average number of inpatient venipunctures per day increased from 1.38 to 1.62 (P < .001), the average number of preanalytic errors per month decreased from 2.24 to 0.16 per 1000 specimens (P < .001). Overall there was a 16% increase in add-on tests. The number of wrong test codes ordered was high and it was challenging for providers to correctly order some common tests., Conclusions: - An interfaced EHR-LIS significantly improved within-laboratory turnaround time and decreased stat requests and preanalytic phlebotomy errors. Despite increasing the number of add-on requests, an electronic add-on process increased efficiency and improved provider satisfaction. Laboratories implementing an interfaced EHR-LIS should be cautious of its effects on test ordering and patient venipunctures per day.