Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial.

BACKGROUND: Overprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model. METHODS: Following qualitative usability testing, we will conduct a stepped-wedge practice-level cluster randomized controlled trial (RCT) examining the effect of iCPR-guided RN care for low acuity patients with ARI. The primary hypothesis to be tested is: Implementation of RN-led iCPR tools will reduce antibiotic prescribing across diverse primary care settings. Specifically, this study aims to: (1) determine the impact of iCPRs on rapid strep test and chest x-ray ordering and antibiotic prescribing rates when used by RNs; (2) examine resource use patterns and cost-effectiveness of RN visits across diverse clinical settings; (3) determine the impact of iCPR-guided care on patient satisfaction; and (4) ascertain the effect of the intervention on RN and physician burnout. DISCUSSION: This study represents an innovative approach to using an iCPR model led by RNs and specifically designed to address inappropriate antibiotic prescribing. This study has the potential to provide guidance on the effectiveness of delegating care of low-acuity patients with ARIs to RNs to increase use of iCPRs and reduce antibiotic overprescribing for ARIs in outpatient settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303 .

Readiness for Delivering Digital Health at Scale: Lessons From a Longitudinal Qualitative Evaluation of a National Digital Health Innovation Program in the United Kingdom.

BACKGROUND: Digital health has the potential to support care delivery for chronic illness. Despite positive evidence from localized implementations, new technologies have proven slow to become accepted, integrated, and routinized at scale. OBJECTIVE: The aim of our study was to examine barriers and facilitators to implementation of digital health at scale through the evaluation of a £37m national digital health program: ?Delivering Assisted Living Lifestyles at Scale" (dallas) from 2012-2015. METHODS: The study was a longitudinal qualitative, multi-stakeholder, implementation study. The methods included interviews (n=125) with key implementers, focus groups with consumers and patients (n=7), project meetings (n=12), field work or observation in the communities (n=16), health professional survey responses (n=48), and cross program documentary evidence on implementation (n=215). We used a sociological theory called normalization process theory (NPT) and a longitudinal (3 years) qualitative framework analysis approach. This work did not study a single intervention or population. Instead, we evaluated the processes (of designing and delivering digital health), and our outcomes were the identified barriers and facilitators to delivering and mainstreaming services and products within the mixed sector digital health ecosystem. RESULTS: We identified three main levels of issues influencing readiness for digital health: macro (market, infrastructure, policy), meso (organizational), and micro (professional or public). Factors hindering implementation included: lack of information technology (IT) infrastructure, uncertainty around information governance, lack of incentives to prioritize interoperability, lack of precedence on accountability within the commercial sector, and a market perceived as difficult to navigate. Factors enabling implementation were: clinical endorsement, champions who promoted digital health, and public and professional willingness. CONCLUSIONS: Although there is receptiveness to digital health, barriers to mainstreaming remain. Our findings suggest greater investment in national and local infrastructure, implementation of guidelines for the safe and transparent use and assessment of digital health, incentivization of interoperability, and investment in upskilling of professionals and the public would help support the normalization of digital health. These findings will enable researchers, health care practitioners, and policy makers to understand the current landscape and the actions required in order to prepare the market and accelerate uptake, and use of digital health and wellness services in context and at scale.

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time window is short. Automated review of electronic health records (EHR) may help, but much of the information is in free text rather than a computable form. We applied natural language processing (NLP) to free text EHR data using the CogStack platform to simulate recruitment into the LeoPARDS study, a clinical trial aiming to reduce organ dysfunction in septic shock. We applied an algorithm to identify eligible patients using a moving 1-hour time window, and compared patients identified by our approach with those actually screened and recruited for the trial, for the time period that data were available. We manually reviewed records of a random sample of patients identified by the algorithm but not screened in the original trial. Our method identified 376 patients, including 34 patients with EHR data available who were actually recruited to LeoPARDS in our centre. The sensitivity of CogStack for identifying patients screened was 90% (95% CI 85%, 93%). Of the 203 patients identified by both manual screening and CogStack, the index date matched in 95 (47%) and CogStack was earlier in 94 (47%). In conclusion, analysis of EHR data using NLP could effectively replicate recruitment in a critical care trial, and identify some eligible patients at an earlier stage, potentially improving trial recruitment if implemented in real time.

Implementation factors affecting the large-scale deployment of digital health and well-being technologies: A qualitative study of the initial phases of the 'Living-It-Up' programme.

Little is known about the factors which facilitate or impede the large-scale deployment of health and well-being consumer technologies. The Living-It-Up project is a large-scale digital intervention led by NHS 24, aiming to transform health and well-being services delivery throughout Scotland. We conducted a qualitative study of the factors affecting the implementation and deployment of the Living-It-Up services. We collected a range of data during the initial phase of deployment, including semi-structured interviews (N = 6); participant observation sessions (N = 5) and meetings with key stakeholders (N = 3). We used the Normalisation Process Theory as an explanatory framework to interpret the social processes at play during the initial phases of deployment.Initial findings illustrate that it is clear - and perhaps not surprising - that the size and diversity of the Living-It-Up consortium made implementation processes more complex within a 'multi-stakeholder' environment. To overcome these barriers, there is a need to clearly define roles, tasks and responsibilities among the consortium partners. Furthermore, varying levels of expectations and requirements, as well as diverse cultures and ways of working, must be effectively managed. Factors which facilitated implementation included extensive stakeholder engagement, such as co-design activities, which can contribute to an increased 'buy-in' from users in the long term. An important lesson from the Living-It-Up initiative is that attempting to co-design innovative digital services, but at the same time, recruiting large numbers of users is likely to generate conflicting implementation priorities which hinder - or at least substantially slow down - the effective rollout of services at scale.The deployment of Living-It-Up services is ongoing, but our results to date suggest that - in order to be successful - the roll-out of digital health and well-being technologies at scale requires a delicate and pragmatic trade-off between co-design activities, the development of innovative services and the efforts allocated to widespread marketing and recruitment initiatives.

Delivering digital health and well-being at scale: lessons learned during the implementation of the dallas program in the United Kingdom.

OBJECTIVE: To identify implementation lessons from the United Kingdom Delivering Assisted Living Lifestyles at Scale (dallas) program-a large-scale, national technology program that aims to deliver a broad range of digital services and products to the public to promote health and well-being. MATERIALS AND METHODS: Prospective, longitudinal qualitative research study investigating implementation processes. Qualitative data collected includes semi-structured e-Health Implementation Toolkit-led interviews at baseline/mid-point (n = 38), quarterly evaluation, quarterly technical and barrier and solutions reports, observational logs, quarterly evaluation alignment interviews with project leads, observational data collected during meetings, and ethnographic data from dallas events (n > 200 distinct pieces of qualitative data). Data analysis was guided by Normalization Process Theory, a sociological theory that aids conceptualization of implementation issues in complex healthcare settings. RESULTS: Five key challenges were identified: 1) The challenge of establishing and maintaining large heterogeneous, multi-agency partnerships to deliver new models of healthcare; 2) The need for resilience in the face of barriers and set-backs including the backdrop of continually changing external environments; 3) The inherent tension between embracing innovative co-design and achieving delivery at pace and at scale; 4) The effects of branding and marketing issues in consumer healthcare settings; and 5) The challenge of interoperability and information governance, when commercial proprietary models are dominant. CONCLUSIONS: The magnitude and ambition of the dallas program provides a unique opportunity to investigate the macro level implementation challenges faced when designing and delivering digital health and wellness services at scale. Flexibility, adaptability, and resilience are key implementation facilitators when shifting to new digitally enabled models of care.

Implementing a National Scottish Digital Health & Wellbeing Service at Scale: A Qualitative Study of Stakeholders' Views.

Digital technologies are being used as part of international efforts to revolutionize healthcare in order to meet increasing demands such as the rising burden of chronic disease and ageing populations. In Scotland there is a government push towards a national service (Living It Up) as a single point of reference where citizens can access information, products and services to support their health and wellbeing. The aim of the study is to examine implementation issues including the challenges or facilitators which can help to sustain this intervention. We gathered data in three ways: a) participant observation to gain an understanding of LiU (N=16); b) in-depth interviews (N=21) with stakeholders involved in the process; and c) analysis of documentary evidence about the progress of the implementation (N=45). Barriers included the need to "work at risk" due to delays in financing, inadequate infrastructure and skill-set deficiencies, whilst facilitators included trusted relationships, champions and a push towards normalisation. The findings suggest that a Scottish ehealth service is achievable but identifies key considerations for future large scale initiatives.