Telehealth-guided provider-to-provider communication to improve rural health: A systematic review.

INTRODUCTION: Telehealth may address healthcare disparities for rural populations. This systematic review assesses the use, effectiveness, and implementation of telehealth-supported provider-to-provider collaboration to improve rural healthcare. METHODS: We searched Ovid MEDLINE®, CINAHL®, EMBASE, and Cochrane CENTRAL from 1 January 2010 to 12 October 2021 for trials and observational studies of rural provider-to-provider telehealth. Abstracts and full text were dual-reviewed. We assessed the risk of bias for individual studies and strength of evidence for studies with similar outcomes. RESULTS: Seven studies of rural uptake of provider-to-provider telehealth documented increases over time but variability across geographic regions. In 97 effectiveness studies, outcomes were similar with rural provider-to-provider telehealth versus without for inpatient consultations, neonatal care, outpatient depression and diabetes, and emergency care. Better or similar results were reported for changes in rural clinician behavior, knowledge, confidence, and self-efficacy. Evidence was insufficient for other clinical uses and outcomes. Sixty-seven (67) evaluation and qualitative studies identified barriers and facilitators to implementing rural provider-to-provider telehealth. Success was linked to well-functioning technology, sufficient resources, and adequate payment. Barriers included lack of understanding of rural context and resources. Methodologic weaknesses of studies included less rigorous study designs and small samples. DISCUSSION: Rural provider-to-provider telehealth produces similar or better results versus care without telehealth. Barriers to rural provider-to-provider telehealth implementation are common to practice change but include some specific to rural adaptation and adoption. Evidence gaps are partially due to studies that do not address differences in the groups compared or do not include sufficient sample sizes.

Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience.

OBJECTIVES: The purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts. METHODS: A collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care. RESULTS: Analysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for "high" appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient. CONCLUSION: Specific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.

Patients' lived experiences and recommendations for enhanced awareness and use of integrative oncology services in cancer care.

BACKGROUND: Consuming educational content, adhering to treatment plans and managing symptoms and side-effects can be overwhelming to new oncology patients. OBJECTIVE: The purpose of this study is to engage patients in conceptualization of enhanced clinic processes and digital health tools to support awareness and use of integrative oncology services. PATIENT INVOLVEMENT: We engaged patients in participatory design to understand lived experiences surrounding use of integrative oncology services during and after conventional cancer treatment. METHODS: Ten participatory design sessions were held with individual participants. Sessions began with patient story telling regarding diagnosis and paths to awareness and use of integrative oncology services. We then reviewed prototype mobile app screens to solicit feedback regarding digital health functionality to support patient navigation of symptom-alleviating options. RESULTS: Oncology patients are active participants in the management of symptoms and side effects. Patients who utilize yoga, acupuncture, and massage report a need for earlier patient education about these services. Patients express interest in digital health tools to match symptoms to options for relief, provide access to searchable information, and facilitate streamlined access to in-person and remote services. DISCUSSION: Patients co-produce wellbeing by seeking solutions to daily challenges and consuming educational content. Clinics can collaborate with patients to identify high priority needs and challenges. PRACTICAL VALUE: Active collaboration with patients is needed to identify unmet needs and guide development of clinic processes and digital health tools to enhance awareness and use of IO services in conventional cancer care. FUNDING: The principal investigator was supported by the U.S. Agency for Healthcare Research and Quality (AHRQ K12HS026370). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. The sponsor had no role in the study design, data collection, analysis, report writing, or decision to submit for publication.

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace.

BACKGROUND: Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain. OBJECTIVES: The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain. METHODS: In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes -8, 10, and 12 hours-were created to assess each model's ability to predict unplanned overtime at various points across the work shift. RESULTS: Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe. CONCLUSION: Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.

Subtle cues: Qualitative elicitation of signs of capacity strain in the hospital workplace.

Through everyday care experiences, nurses develop expertise in recognition of capacity strain in hospital workplaces. Through qualitative interview, experienced nurses identify common activity changes and adaptive work strategies that may signal an imbalance between patient demand and service supply at the bedside. Activity change examples include nurse helping behaviors across patient assignments, increased volume of nurse calls from patient rooms, and decreased presence of staff at the nurses' station. Adaptive work strategies encompass actions taken to recruit resources, move work in time, reduce work demands, or reduce thoroughness of task performance. Nurses' knowledge of perceptible signs of strain provides a foundation for future exploration and development of real-time indicators of capacity strain in hospital-based work systems.

Current practices in clinical analytics: a hospital survey report.

Clinical analytics must become a pervasive activity in healthcare settings to achieve the global vision for timely, effective, equitable, and excellent care. Global adoption of the Electronic Health Record (EHR) has increased the volume of data available for performance measurement and healthcare organizational capacity for continuous quality improvement. However, EHR adoption does not automatically result in optimal use of clinical data for performance improvement. In order to understand organizational factors related to use of data for clinical analytics, a survey was conducted of hospitals and hospital-based clinics. The survey revealed sub-optimal use of data captured as a byproduct of care delivery, the need for tools and methodologies to assist with data analytics, and the need for disciplined organizational structure and strategies. Informatics nurse professionals are well-positioned to lead analytical efforts and serve as a catalyst in their facility's transformations into a data-driven organization.