Equity-Centered Postdischarge Support for Medicaid-Insured People: Protocol for a Type 1 Hybrid Effectiveness-Implementation Stepped Wedge Cluster Randomized Controlled Trial.

BACKGROUND: Disparities in posthospitalization outcomes for people with chronic medical conditions and insured by Medicaid are well documented, yet interventions that mitigate them are lacking. Prevailing transitional care interventions narrowly target people aged 65 years and older, with specific disease processes, or limitedly focus on individual-level behavioral change such as self-care or symptom management, thus failing to adequately provide a holistic approach to ensure an optimal posthospital care continuum. This study evaluates the implementation of THRIVE-an evidence-based, equity-focused clinical pathway that supports Medicaid-insured individuals with multiple chronic conditions transitioning from hospital to home by focusing on the social determinants of health and systemic and structural barriers in health care delivery. THRIVE services include coordinating care, standardizing interdisciplinary communication, and addressing unmet clinical and social needs following hospital discharge. OBJECTIVE: The study's objectives are to (1) examine referral patterns, 30-day readmission, and emergency department use for participants who receive THRIVE support services compared to those receiving usual care and (2) evaluate the implementation of the THRIVE clinical pathway, including fidelity, feasibility, appropriateness, and acceptability. METHODS: We will perform a sequential randomized rollout of THRIVE to case managers at the study hospital in 3 steps (4 in the first group, 4 in the second, and 5 in the third), and data collection will occur over 18 months. Inclusion criteria for THRIVE participation include (1) being Medicaid insured, dually enrolled in Medicaid and Medicare, or Medicaid eligible; (2) residing in Philadelphia; (3) having experienced a hospitalization at the study hospital for more than 24 hours with a planned discharge to home; (4) agreeing to home care at partner home care settings; and (5) being aged 18 years or older. Qualitative data will include interviews with clinicians involved in THRIVE, and quantitative data on health service use (ie, 30-day readmission, emergency department use, and primary and specialty care) will be derived from the electronic health record. RESULTS: This project was funded in January 2023 and approved by the institutional review board on March 10, 2023. Data collection will occur from March 2023 to July 2024. Results are expected to be published in 2025. CONCLUSIONS: The THRIVE clinical pathway aims to reduce disparities and improve postdischarge care transitions for Medicaid-insured patients through a system-level intervention that is acceptable for THRIVE participants, clinicians, and their teams in hospitals and home care settings. By using our equity-focused case management services and leveraging the power of the electronic medical record, THRIVE creates efficiencies by identifying high-need patients, improving communication across acute and community-based sectors, and driving evidence-based care coordination. This study will add important findings about how the infusion of equity-focused principles in the design and evaluation of evidence-based interventions contributes to both implementation and effectiveness outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54211. TRIAL REGISTRATION: ClinicalTrials.gov NCT05714605; https://clinicaltrials.gov/ct2/show/NCT05714605.

The Impact of Health Care Algorithms on Racial and Ethnic Disparities : A Systematic Review.

BACKGROUND: There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities. PURPOSE: To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities. DATA SOURCES: Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023. STUDY SELECTION: Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. DATA EXTRACTION: Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension. DATA SYNTHESIS: Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques. LIMITATION: Results are mostly based on modeling studies and may be highly context-specific. CONCLUSION: Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes. PRIMARY FUNDING SOURCE: Agency for Healthcare Quality and Research.

Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models Extension: Development of a Critical Appraisal Tool Extension to Assess Racial and Ethnic Equity-Related Risk of Bias for Clinical Prediction Models.

Introduction: Despite mounting evidence that the inclusion of race and ethnicity in clinical prediction models may contribute to health disparities, existing critical appraisal tools do not directly address such equity considerations. Objective: This study developed a critical appraisal tool extension to assess algorithmic bias in clinical prediction models. Methods: A modified e-Delphi approach was utilized to develop and obtain expert consensus on a set of racial and ethnic equity-based signaling questions for appraisal of risk of bias in clinical prediction models. Through a series of virtual meetings, initial pilot application, and an online survey, individuals with expertise in clinical prediction model development, systematic review methodology, and health equity developed and refined this tool. Results: Consensus was reached for ten equity-based signaling questions, which led to the development of the Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models (CARE-CPM) extension. This extension is intended for use along with existing critical appraisal tools for clinical prediction models. Conclusion: CARE-CPM provides a valuable risk-of-bias assessment tool extension for clinical prediction models to identify potential algorithmic bias and health equity concerns. Further research is needed to test usability, interrater reliability, and application to decision-makers.

Clostridioides difficile dynamic electronic order panel, an effective automated intervention to reduce inappropriate inpatient ordering.

BACKGROUND: Ordering Clostridioides difficile diagnostics without appropriate clinical indications can result in inappropriate antibiotic prescribing and misdiagnosis of hospital onset C. difficile infection. Manual processes such as provider review of order appropriateness may detract from other infection control or antibiotic stewardship activities. METHODS: We developed an evidence-based clinical algorithm that defined appropriateness criteria for testing for C. difficile infection. We then implemented an electronic medical record-based order-entry tool that utilized discrete branches within the clinical algorithm including history of prior C. difficile test results, laxative or stool-softener administration, and documentation of unformed bowel movements. Testing guidance was then dynamically displayed with supporting patient data. We compared the rate of completed C. difficile tests after implementation of this intervention at 5 hospitals to a historic baseline in which a best-practice advisory was used. RESULTS: Using mixed-effects Poisson regression, we found that the intervention was associated with a reduction in the incidence rate of both C. difficile ordering (incidence rate ratio [IRR], 0.74; 95% confidence interval [CI], 0.63-0.88; P = .001) and C. difficile-positive tests (IRR, 0.83; 95% CI, 0.76-0.91; P < .001). On segmented regression analysis, we identified a sustained reduction in orders over time among academic hospitals and a new reduction in orders over time among community hospitals. CONCLUSIONS: An evidence-based dynamic order panel, integrated within the electronic medical record, was associated with a reduction in both C. difficile ordering and positive tests in comparison to a best practice advisory, although the impact varied between academic and community facilities.

The Differential Paracrine Role of the Endothelium in Prostate Cancer Cells.

The survival of patients with solid tumors, such as prostate cancer (PCa), has been limited and fleeting with anti-angiogenic therapies. It was previously thought that the mechanism by which the vasculature regulates tumor growth was driven by a passive movement of oxygen and nutrients to the tumor tissue. However, previous evidence suggests that endothelial cells have an alternative role in changing the behavior of tumor cells and contributing to cancer progression. Determining the impact of molecular signals/growth factors released by endothelial cells (ECs) on established PCa cell lines in vitro and in vivo could help to explain the mechanism by which ECs regulate tumor growth. Using cell-conditioned media collected from HUVEC (HUVEC-CM), our data show the stimulated proliferation of all the PCa cell lines tested. However, in more aggressive PCa cell lines, HUVEC-CM selectively promoted migration and invasion in vitro and in vivo. Using a PCa-cell-line-derived xenograft model co-injected with HUVEC or preincubated with HUVEC-CM, our results are consistent with the in vitro data, showing enhanced tumor growth, increased tumor microvasculature and promoted metastasis. Gene set enrichment analyses from RNA-Seq gene expression profiles showed that HUVEC-CM induced a differential effect on gene expression when comparing low versus highly aggressive PCa cell lines, demonstrating epigenetic and migratory pathway enrichments in highly aggressive PCa cells. In summary, paracrine stimulation by HUVEC increased PCa cell proliferation and tumor growth and selectively promoted migration and metastatic potential in more aggressive PCa cell lines.

Castleman's disease: Laparoscopic resection of unicentric disease in the hepatic hilum: A case report.

INTRODUCTION AND IMPORTANCE: Castleman's disease was first reported by Benjamin Castleman et al., in 1954 and described it as a sporadic lymphoproliferative disorder. The pathophysiology to this day is still unknown, although IL-6 is suspected to play an important role. Preoperative diagnosis is challenging due to its non-specific symptoms, and that imaging cannot clearly distinguish the disease from other processes. High clinical awareness is necessary to reach a diagnosis. If the disease is localized, complete recovery can be achieved through surgery. CASE PRESENTATION: Patient is a 68-year-old woman with a three-month history of recurrent episodes of fever, myalgias, and night sweats. She started to experience lower abdominal pain and presented to the emergency room. A contrast-enhanced abdominal computed tomography revealed a 5 cm well-circumscribed focal heterogeneously enhancing hyperplastic mass between the portal vein and the inferior vena cava. After successful laparoscopic surgery, the mass was resected, and the patient fully recovered. Unicentric Castleman's disease was the final diagnosis. DISCUSSION AND CONCLUSION: Castleman's disease is an uncommon pathology with a challenging diagnosis. When approaching an abdominal mass, unicentric Castleman's disease should always be a differential diagnosis, as treatment can be curative with surgical resection. With the advent of laparoscopic and robotic surgery, these techniques can improve patients' outcomes in these rare pathologies, especially when they appear in complex regions.

Translating an evidence-based clinical pathway into shareable CDS: developing a systematic process using publicly available tools.

OBJECTIVE: To develop a process for translating semi-structured clinical decision support (CDS) into shareable, computer-readable CDS. MATERIALS AND METHODS: We developed a systematic and transparent process using publicly available tools (eGLIA, GEM Cutter, VSAC, and the CDS Authoring Tool) to translate an evidence-based clinical pathway (CP) into a Clinical Quality Language (CQL)-encoded CDS artifact. RESULTS: We produced a 4-phase process for translating a CP into a CQL-based CDS artifact. CP content was extracted using GEM into discrete clinical concepts, encoded using standard terminologies into value sets on VSAC, evaluated against workflows using a wireframe, and finally structured as a computer readable CDS artifact using CQL. This process included a quality control step and intermediate products to support transparency and reuse by other CDS developers. DISCUSSION: Translating a CP into a shareable, computer-readable CDS artifact was accomplished through a systematic process. Our process identified areas of ambiguity and gaps in the CP, which generated improvements in the CP. Collaboration with clinical subject experts and the CP development team was essential for translation. Publicly available tools were sufficient to support most translation steps, but expression of certain complex concepts required manual encoding. CONCLUSION: Standardized development of CDS from a CP is feasible using a systematic 4-phase process. CPs represent a potential reservoir for developers of evidence-based CDS. Aspects of CP development simplified portions of the CDS translation process. Publicly available tools can facilitate CDS development; however, enhanced tool features are needed to model complex CDS statements.

AHRQ EPC Series on Improving Translation of Evidence: Use of a Clinical Pathway for C. Difficile Treatment to Facilitate the Translation of Research Findings into Practice.

BACKGROUND: In 2018 the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program issued a call for strategies to disseminate AHRQ EPC systematic reviews. In this pilot, findings from the 2016 AHRQ EPC report on Clostridioides difficile infection were translated into a treatment pathway and disseminated via a cloud-based platform and electronic health record (EHR). METHODS: An existing 10-step framework was used for developing and disseminating evidence-based clinical pathways. The development of the EHR intervention was informed by the Five Rights model for clinical decision support and human-computer interaction design heuristics. The researchers used observations and time measurements to describe the impact of the EPC report on pathway development and examined provider adoption using counts of pathway views. RESULTS: Two main themes emerged: (1) discrepancies between the EPC report and existing guidelines prompted critical discussions about available treatments, and (2) lack of guideline and pathway syntheses in the EPC report necessitated a rapid literature review. Pathway development required 340 hours: 205 for the rapid literature review, 63 for pathway development and EHR intervention design, and 5 for technical implementation of the intervention. Pathways were viewed 1,069 times through the cloud-based platform and 47 times through a hyperlink embedded in key EHR ordering screens. CONCLUSION: Pathways can be an approach for disseminating AHRQ EPC report findings within health care systems; however, reports should include guideline and pathway syntheses to meet their full potential. Embedding hyperlinks to pathway content within the EHR may be a viable and low-effort solution for promoting awareness of evidence-based resources.

Next Steps in Improving Healthcare Value: AHRQ Evidence-based Practice Center Program-Applying the Knowledge to Practice to Data Cycle to Strengthen the Value of Patient Care.

For more than 20 years, the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program has been identifying and synthesizing evidence to inform evidence-based healthcare. Recognizing that many healthcare settings continue to face challenges in disseminating and implementing evidence into practice, AHRQ's EPC program has also embarked on initiatives to facilitate the translation of evidence into practice and to measure and monitor how practice changes impact health outcomes. The program has structured its efforts around the three phases of the Learning Healthcare System cycle: knowledge, practice, and data. Here, we use a topic relevant to the field of hospital medicine-Clostridium difficile colitis prevention and treatment-as an exemplar of how the EPC program has used this framework to move evidence into practice and develop systems to facilitate continuous learning in healthcare systems.

Using a 10-step framework to support the implementation of an evidence-based clinical pathways programme.

BACKGROUND: Integration of evidence into practice is suboptimal. Clinical pathways, defined as multidisciplinary care plans, are a method for translating evidence into local settings and have been shown to improve the value of patient care. OBJECTIVE: To describe the development of a clinical pathways programme across a large academic healthcare system. METHODS: We use a 10-step framework (grounded in the Knowledge-to-Action framework and ADAPTE Collaboration methodology for guideline adaptation) to support pathway development and dissemination, including facilitating clinical owner and stakeholder engagement, developing pathway prototypes based on rapid reviews of the existing literature, developing tools for dissemination and impact assessment. We use a cloud-based technology platform (Dorsata, Washington, DC) to assist with development and dissemination across our geographically distributed care settings and providers. Content is viewable through desktop and mobile applications. We measured programme adoption and penetration by examining number of pathways developed as well as mobile application use and pathway views. RESULTS: From 1 February 2016 to 30 April 2018, a total of 202 pathways were disseminated. The three most common clinical domains represented were oncology (46.5%, n=94), pulmonary/critical care (8.9%, n=18) and cardiovascular medicine (7.4%, n=15). Users opting to register for a personal account totalled 1279; the three largest groups were physicians (45.1%, n=504), advanced practice providers (19.5%, n=245) and nurses (19.1%, n=240). Pathway views reached an average of 2150 monthly views during the last 3 months of the period. The majority of pathways reference at least one evidence-based source (93.6%, n=180). CONCLUSIONS: A healthcare system can successfully use a framework and technology platform to support the development and dissemination of pathways across a multisite institution.

Using a Decision Support Algorithm for Referrals to Post-Acute Care.

OBJECTIVES: Although hospital clinicians strive to effectively refer patients who require post-acute care (PAC), their discharge planning processes often vary greatly, and typically are not evidence-based. DESIGN: Quasi-experimental study employing pre-/postdesign. Aimed at improving patient-centered discharge processes, we examined the effects of the Discharge Referral Expert System for Care Transitions (DIRECT) algorithm that provides clinical decision support (CDS) regarding which patients to refer to PAC and to what level of care (home care or facility). SETTING AND PARTICIPANTS: Conducted in 2 hospitals, DIRECT data elements were collected in the pre-period (control) but discharging clinicians were blinded to the advice and provided usual discharge care. During the postperiod (intervention), referral advice was provided within 24 hours of admission to clinicians, and updated twice daily. Propensity modeling was used to account for differences between the pre-/post patient cohorts. MEASURES: Outcomes compared between the control and the intervention periods included PAC referral rates, patient characteristics, and same-, 7-, 14-, and 30-day readmissions or emergency department visits. RESULTS: Although 24%-25% more patients were recommended for PAC referral by DIRECT algorithm advice, the proportion of patients receiving referrals for PAC did not significantly differ between the control (3302) and intervention (5006) periods. However, the characteristics of patients referred for PAC services differed significantly and inpatient readmission rates decreased significantly across all time intervals when clinicians had DIRECT CDS compared with without. There were no differences observed in return emergency department visits. Largest effects were observed when clinicians agreed with the algorithm to refer (yes/yes). CONCLUSIONS/IMPLICATIONS: Our findings suggest the value of timely, automated, discharge CDS for clinicians to optimize PAC referral for those most likely to benefit. Although overall referral rates did not change with CDS, the algorithm may have identified those patients most in need, resulting in significantly lower inpatient readmission rates.

Nurse Generated EHR Data Supports Post-Acute Care Referral Decision Making: Development and Validation of a Two-step Algorithm.

Objective: Build and validate a clinical decision support (CDS) algorithm for discharge decisions regarding referral for post-acute care (PAC) and to what site of care. Materials and Methods: Case studies derived from EHR data were judged by 171 interdisciplinary experts and prediction models were generated. Results: A two-step algorithm emerged with area under the curve (AUC) in validation of 91.5% (yes/no refer) and AUC 89.7% (where to refer). Discussion: CDS for discharge planning (DP) decisions may remove subjectivity, and variation in decision-making. CDS could automate the assessment process and alert clinicians of high need patients earlier in the hospital stay. Conclusion: Our team successfully built and validated a two-step algorithm to support discharge referral decision-making from EHR data. Getting patients the care and support they need may decrease readmissions and other adverse events. Further work is underway to test the effects of the CDS on patient outcomes in two hospitals.

Why older adults may decline offers of post-acute care services: A qualitative descriptive study.

The most common post-acute care (PAC) services available to patients after hospital discharge include home care, skilled nursing facilities, nursing homes, inpatient rehabilitation, and hospice. Patients who need PAC and receive services have better outcomes, however almost one-third of those offered services decline. Little research exists on PAC decision-making and why patients may decline services. This qualitative descriptive study explored the responses of thirty older adults to the question: "Can you, from the patient point of view, tell me why someone would not want post hospital care?" Three themes emerged. Participants may decline due to 1) previous negative experiences with PAC, or 2) a preference to be home. Some participants stated, "I'd be there" and would not decline services. Participants also discussed 3) why other patients might decline PAC which included patients' past experiences, lack of understanding/preconceived ideas, and preferences. Clinical implications include assessing patients' knowledge and experience before providing recommendations.

Patient Preferences for Information on Post-Acute Care Services.

The purpose of the current study was to explore what hospitalized patients would like to know about post-acute care (PAC) services to ultimately help them make an informed decision when offered PAC options. Thirty hospitalized adults 55 and older in a Northeastern U.S. academic medical center participated in a qualitative descriptive study with conventional content analysis as the analytical technique. Three themes emerged: (a) receiving practical information about the services, (b) understanding "how it relates to me," and (c) having opportunities to understand PAC options. Study findings inform clinicians what information should be included when discussing PAC options with older adults. Improving the quality of discharge planning discussions may better inform patient decision making and, as a result, increase the numbers of patients who accept a plan of care that supports recovery, meets their needs, and results in improved quality of life and fewer readmissions. [Res Gerontol Nurs. 2016; 9(4):175-182.].