Supporting ColoREctal Equitable Navigation (SCREEN): a protocol for a stepped-wedge cluster randomized trial for patient navigation in primary care.

BACKGROUND: Black individuals in the United States (US) have a higher incidence of and mortality from colorectal cancer (CRC) compared to other racial groups, and CRC is the second leading cause of death among Hispanic/Latino populations in the US. Patient navigation is an evidence-based approach to narrow inequities in cancer screening among Black and Hispanic/Latino patients. Despite this, limited healthcare systems have implemented patient navigation for screening at scale. METHODS: We are conducting a stepped-wedge cluster randomized trial of 15 primary care clinics with six steps of six-month duration to scale a patient navigation program to improve screening rates among Black and Hispanic/Latino patients. After six months of baseline data collection with no intervention we will randomize clinics, whereby three clinics will join the intervention arm every six months until all clinics cross over to intervention. During the intervention roll out we will conduct training and education for clinics, change infrastructure in the electronic health record, create stakeholder relationships, assess readiness, and deliver iterative feedback. Framed by the Practical, Robust Implementation Sustainment Model (PRISM) we will focus on effectiveness, reach, provider adoption, and implementation. We will document adaptations to both the patient navigation intervention and to implementation strategies. To address health equity, we will engage multilevel stakeholder voices through interviews and a community advisory board to plan, deliver, adapt, measure, and disseminate study progress. Provider-level feedback will include updates on disparities in screening orders and completions. DISCUSSION: Primary care clinics are poised to close disparity gaps in CRC screening completion but may lack an understanding of the magnitude of these gaps and how to address them. We aim to understand how to tailor a patient navigation program for CRC screening to patients and providers across diverse clinics with wide variation in baseline screening rates, payor mix, proximity to specialty care, and patient volume. Findings from this study will inform other primary care practices and health systems on effective and sustainable strategies to deliver patient navigation for CRC screening among racial and ethnic minorities. TRIAL REGISTRATION: NCT06401174.

Contextual Factors Relevant to Implementing Social Risk Factor Screening and Referrals in Cancer Survivorship: A Qualitative Study.

Introduction: Social risk factors such as food insecurity and lack of transportation can negatively affect health outcomes, yet implementation of screening and referral for social risk factors is limited in medical settings, particularly in cancer survivorship. Methods: We conducted 18 qualitative, semistructured interviews among oncology teams in 3 health systems in Washington, DC, during February and March 2022. We applied the Exploration, Preparation, Implementation, Sustainment Framework to develop a deductive codebook, performed thematic analysis on the interview transcripts, and summarized our results descriptively. Results: Health systems varied in clinical and support staff roles and capacity. None of the participating clinics had an electronic health record (EHR)-based process for identifying patients who completed their cancer treatment ("survivors") or a standardized cancer survivorship program. Their capacities also differed for documenting social risk factors and referrals in the EHR. Interviewees expressed awareness of the prevalence and effect of social risk factors on cancer survivors, but none employed a systematic process for identifying and addressing social risk factors. Recommendations for increasing screening for social risk factors included designating a person to fulfill this role, improving data tracking tools in the EHR, and creating systems to maintain up-to-date information and contacts for community-based organizations. Conclusion: The complexity of cancer care workflows and lack of reimbursement results in a limited ability for clinic staff members to screen and make referrals for social risk factors. Creating clinical workflows that are flexible and tailored to staffing realities may contribute to successful implementation of a screening and referral program. Improving ongoing communication with community-based organizations to address needs was deemed important by interviewees.

Display and perception of risk: Analysis of decision support system display and its impact on perceived clinical risk of sepsis-induced health deterioration.

Despite acknowledging the value of clinical decision support systems (CDSS) in identifying risk for sepsis-induced health deterioration in-hospitalized patients, the relationship between display features, decision maker characteristics, and recognition of risk by the clinical decision maker remains an understudied, yet promising, area. The objective of this study is to explore the relationship between CDSS display design and perceived clinical risk of in-hospital mortality associated with sepsis. The study utilized data collected through in-person experimental sessions with 91 physicians from the general medical and surgical floors who were recruited across 12 teaching hospitals within the United States. Results of descriptive and statistical analyses provided evidence supporting the impact of display configuration and clinical case severity on perceived risk associated with in-hospital mortality. Specifically, findings showed that a high level of information (represented by the Predisposition, Infection, Response and Organ dysfunction (PIRO) score) and Figure display (as opposed to Text or baseline) increased awareness to recognizing the risk for in-hospital mortality of hospitalized sepsis patients. A CDSS display that synthesizes the optimal features associated with information level and design elements has the potential to enhance the quantification and communication of clinical risk in complex health conditions beyond sepsis.

The Available Criteria for Different Sepsis Scoring Systems in the Emergency Department-A Retrospective Assessment.

OBJECTIVE: The goal of the study was to assess the criteria availability of eight sepsis scoring methods within 6 hours of triage in the emergency department (ED). DESIGN: Retrospective data analysis study. SETTING: ED of MedStar Washington Hospital Center (MWHC), a 912-bed urban, tertiary hospital. PATIENTS: Adult (age ≥ 18 years) patients presenting to the MWHC ED between June 1, 2017 and May 31, 2018 and admitted with a diagnosis of severe sepsis with or without shock. MAIN OUTCOMES MEASURED: Availability of sepsis scoring criteria of eight different sepsis scoring methods at three time points-0 Hours (T0), 3 Hours (T1) and 6 Hours (T2) after arrival to the ED. RESULTS: A total of 50 charts were reviewed, which included 23 (46%) males and 27 (54%) females. Forty-eight patients (96%) were Black or African American. Glasgow Coma Scale was available for all 50 patients at T0. Vital signs, except for temperature, were readily available (>90%) at T0. The majority of laboratory values relevant for sepsis scoring criteria were available (>90%) at T1, with exception to bilirubin (66%) and creatinine (80%). NEWS, PRESEP and qSOFA had greater than 90% criteria availability at triage. SOFA and SIRS consistently had the least percent of available criteria at all time points in the ED. CONCLUSION: The availability of patient data at different time points in a patient's ED visit suggests that different scoring methods could be utilized to assess for sepsis as more patient information becomes available.

Population subgroup differences in the use of a COVID-19 chatbot.

COVID-19 chatbots are widely used to screen for symptoms and disseminate information about the virus, yet little is known about the population subgroups that interact with this technology and the specific features that are used. An analysis of 1,000,740 patients invited to use a COVID-19 chatbot, 69,451 (6.94%) of which agreed to participate, shows differences in chatbot feature use by gender, race, and age. These results can inform future public health COVID-19 symptom screening and information dissemination strategies.

Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis.

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance. We developed graphical model displays of two popular sepsis scoring systems, quick Sepsis Related Organ Failure Assessment and Predisposition, Infection, Response, Organ Failure, using human factors principles grounded in user-centered and interaction design. Models will be evaluated in a larger research effort to optimize alert design to improve the collective awareness of high-risk populations and develop a relevant point-of-care clinical decision support system for sepsis.

A socio-technical systems approach to the use of health IT for patient reported outcomes: Patient and healthcare provider perspectives.

BACKGROUND: Patient-Reported Outcomes (PROs) can be used to inform the clinical management of individuals, including patient self-management, care planning, and goal setting. Despite a rapid proliferation of technology to collect and integrate PROs in clinical care, uptake by patients and healthcare providers remains sub optimal. A consideration of systems factors to understand these challenges is needed. OBJECTIVES: To apply the socio-technical systems (STS) model as a framework for understanding the usability and functional requirements of patients collecting PRO data using applications (apps), and of healthcare providers using these data at the point of care in ambulatory settings. METHODS: With questions guided by the STS model, semi-structured interviews were conducted with eighteen patients and nine healthcare providers to elicit feedback about facilitators and barriers to successful use of PRO apps and PRO data in ambulatory settings. Patient participants were selected to fit into two categories: older, low utilizers of technology with less than a bachelor's degree, and younger higher utilizers of technology with at least a bachelor's degree. Participants were from primary and specialty care practices. Data were analyzed inductively to identify emergent themes. RESULTS: Younger patients were only interested in using a PRO app if they had an active health issue to track. The nine older patients preferred passive means of data collection if they were to track a health issue, and preferred direct contact with their healthcare provider and using office visits to share information. All patients desired optimal usability and emphasized bidirectional communication in an app that is transparent about privacy. All nine healthcare providers agreed that PRO data would be most useful and relevant if key patient populations were targeted based on the specific measure. In this case the healthcare providers noted potentially optimal utility of collecting physical function PRO data for patients 65 and older. Access to the data was highlighted by each healthcare provider stating that these data would be most useful if they were seamlessly integrated into the electronic health record. DISCUSSION: Several emergent themes were identified under the five selected dimensions of the STS model (clinical content, human computer interface, hardware and software computing infrastructure, people, and workflow and communication). Findings highlighted the continued need for innovative methods to obtain more rapid cycle, continuous feedback to identify system factors impacting use of these technologies. CONCLUSION: The STS model provides a comprehensive framework that can be applied to collect patient and healthcare provider feedback to better guide the design and implementation of new health information technology.

Risky Business: Predicting patient mortality at a glance.

Clinicians are constantly forecasting patient trajectories to make critical point of care decisions intended to influence clinical outcomes. Little is known, however, about how providers interpret mortality risk against validated scoring systems. This research aims to understand how providers forecast mortality specifically for that of patients with sepsis. Defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, sepsis is commonly hard to diagnose, progresses rapidly, and lacks a "gold standard" test. Participants were nurses and doctors from the general medical and surgical floors of six different hospitals. Each was presented with ten different patient cases, categorized into low and high severity sepsis, and were asked about care decisions, along with estimations of mortality risk. The resulting data provides a unique look into the differences of risk forecasting between profession and patient severity.

Score at the Door: A retrospective data analysis of sepsis scoring criteria in the Emergency Department.

The goal of this study was to assess the availability of the criteria of eight sepsis scoring methods (SIRS, NEWS, PRESEP, SOFA, qSOFA, SPEED, MEDS, and PIRO) within six hours of triage in the emergency department (ED). Data was analyzed through a retrospective collection of adult (age >18 years) patients presenting to the MedStar Washington Hospital Center (MWHC) emergency department between June 1, 2017 and May 31, 2018 and admitted with a sepsis or sepsis-related diagnosis that progressed to sepsis before discharge. Vital signs are frequently available upon arrival to the ED, while laboratory values tend to be available within three hours and often are not repeated again within the first six hours after arrival. The availability of patient data at different time points in a patient's ED visit suggests that different scoring methods could be utilized to more effectively diagnose and accurately risk-stratify patients within the sepsis spectrum as more information about the patient becomes available.

Objective measures of workload in healthcare: a narrative review.

PURPOSE: Workload is a critical concept in the evaluation of performance and quality in healthcare systems, but its definition relies on the perspective (e.g. individual clinician-level vs unit-level workload) and type of available metrics (e.g. objective vs subjective measures). The purpose of this paper is to provide an overview of objective measures of workload associated with direct care delivery in tertiary healthcare settings, with a focus on measures that can be obtained from electronic records to inform operationalization of workload measurement. DESIGN/METHODOLOGY/APPROACH: Relevant papers published between January 2008 and July 2018 were identified through a search in Pubmed and Compendex databases using the Sample, Phenomenon of Interest, Design, Evaluation, Research Type framework. Identified measures were classified into four levels of workload: task, patient, clinician and unit. FINDINGS: Of 30 papers reviewed, 9 used task-level metrics, 14 used patient-level metrics, 7 used clinician-level metrics and 20 used unit-level metrics. Key objective measures of workload include: patient turnover (n=9), volume of patients (n=6), acuity (n=6), nurse-to-patient ratios (n=5) and direct care time (n=5). Several methods for operationalization of these metrics into measurement tools were identified. ORIGINALITY/VALUE: This review highlights the key objective workload measures available in electronic records that can be utilized to develop an operational approach for quantifying workload. Insights gained from this review can inform the design of processes to track workload and mitigate the effects of increased workload on patient outcomes and clinician performance.