A practical framework for achieving value creation and capture in healthcare through process improvement

PurposeThe purpose of this study was to examine the concept of value in healthcare through a practical appraisal of the applicability of a conceptual framework, which is aimed at supporting the measurement and realisation of financial benefits from process improvement (PI) activities in a hospital setting.Design/methodology/approachA single case study of a hospital system in Melbourne, Victoria, Australia, was used to assess the applicability of the framework. The study sought to verify the framework's intention, that PI methods could be used to address known wastes that contribute to the cost of providing healthcare. The case study examines the current approach taken by the hospital to measure and realise financial benefits from PI activities and compares these to the components of the Strategy to Balance Cost and Quality in Health Care framework to assess its applicability in practice.FindingsThe case study revealed that the steps described in the framework were fundamentally in place albeit with some variation. Importantly, the case study identified an additional step that could be added into the framework to support hospitals to better define their portfolio of initiatives to deliver value. The case study also clarified three types of contributory elements that should be in place for the application of the framework to be successful.Practical implicationsThe Framework to Achieve Value in Healthcare is offered to hospitals as a model by which they can look to reduce expenditure through the removal of non-value adding activities. The modification to the conceptual framework has arisen from a single case study and would benefit from further testing by other hospitals in other policy settings (i.e. other countries).Originality/valueThis is the first paper to examine and enhance an existing framework to assist hospitals balance cost and quality through PI.

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

Objective To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). Methods We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict “severe” COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. Results The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed “severe” COVID-19. Patients in the highest quintile developed “severe” COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). Conclusion A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Fully Vaccinated and Boosted Patients Requiring Hospitalization for COVID-19: an Observational Cohort Analysis

Objective: Real-world data on the effectiveness of boosters against COVID-19, especially as new variants continue to emerge, is limited. It is our objective to assess demographic, clinical, and outcome variables of patients requiring hospitalization for severe SARS-CoV-2 infection comparing fully vaccinated and boosted (FV&B) and unvaccinated (UV) patients. Methods: This multicenter observational cohort analysis compared demographic, clinical, and outcome variables in FV&B and UV adults hospitalized for COVID-19. A sub-analysis of FV&B patients requiring intensive care (ICU) care versus non-ICU care was performed to describe and analyze common symptom presentations, initial vital signs, initial laboratory workup, and pertinent medication use in these two groups. Results: Between August 12th, 2021 and December 6th, 2021, 4,571 patient encounters had a primary diagnosis of COVID-19 and required inpatient treatment at an acute-care hospital system in Southeastern Michigan. Of the 4,571 encounters requiring hospitalization, 65(1.4%) were FV&B and 2,935(64%) were UV. FV&B individuals were older (74 [67, 81] vs 58 [45, 70]; p <0.001) with a higher proportion of immunocompromised individuals (32.3% vs 10.4%; p<0.001). Despite a significantly higher baseline risk of in-hospital mortality in the FV&B group compared to the UV (Elixhauser 16 vs 8 (p <0.001)), there was a trend toward lower in-hospital mortality (7.7% vs 12.1%; p=0.38) among FV&B patients. Other severe outcomes followed this same trend, with 7.7% of FV&B vs 11.1% UV patients needing mechanical ventilation and 4.6% vs 10.6% of patients needing vasopressors in each group, respectively (p=0.5 and 0.17). Conclusions: Fully vaccinated and boosted individuals requiring hospital-level care for breakthrough COVID-19 tended to have less severe outcomes despite appearing to be higher risk at baseline when compared to unvaccinated individuals during the same time period. Specifically, there was a trend that FV&B group had lower rates of mechanical ventilation, use of vasopressors, and in-hospital mortality. As COVID-19 continues to spread, larger expansive trials are needed to further identify risk factors for severe outcomes among the FV&B population.

Iowa Implementation for Sustainability Framework

Background An application-oriented implementation framework designed for clinicians and based on the Diffusion of Innovations theory included 81 implementation strategies with suggested timing for use within four implementation phases. The purpose of this research was to evaluate and strengthen the framework for clinician use and propose its usefulness in implementation research. Methods A multi-step, iterative approach guided framework revisions. Individuals requesting the use of the framework over the previous 7 years were sent an electronic questionnaire. Evaluation captured framework usability, generalizability, accuracy, and implementation phases for each strategy. Next, nurse leaders who use the framework pile sorted strategies for cultural domain analysis. Last, a panel of five EBP/implementation experts used these data and built consensus to strengthen the framework. Results Participants (n = 127/1578; 8% response) were predominately nurses (94%), highly educated (94% Master’s or higher), and from across healthcare (52% hospital/system, 31% academia, and 7% community) in the USA (84%). Most (96%) reported at least some experience using the framework and 88% would use the framework again. A 4-point scale (1 = not/disagree to 4 = very/agree) was used. The framework was deemed useful (92%, rating 3–4), easy to use (72%), intuitive (67%), generalizable (100%), flexible and adaptive (100%), with accurate phases (96%), and accurate targets (100%). Participants (n = 51) identified implementation strategy timing within four phases (Cochran’s Q); 54 of 81 strategies (66.7%, p < 0.05) were significantly linked to a specific phase; of these, 30 (55.6%) matched the original framework. Next, nurse leaders (n = 23) completed a pile sorting activity. Anthropac software was used to analyze the data and visualize it as a domain map and hierarchical clusters with 10 domains. Lastly, experts used these data and implementation science to refine and specify each of the 75 strategies, identifying phase, domain, actors, and function. Strategy usability, timing, and groupings were used to refine the framework. Conclusion The Iowa Implementation for Sustainability Framework offers a typology to guide implementation for evidence-based healthcare. This study specifies 75 implementation strategies within four phases and 10 domains and begins to validate the framework. Standard use of strategy names is foundational to compare and understand when implementation strategies are effective, in what dose, for which topics, by whom, and in what context.

Stakeholders' Perception of the Introduction of Specialized Hospitals for Urologic Diseases: Qualitative Study

Purpose: The purpose of this study is to seek in-depth perspectives of stakeholders on the necessity and specific criteria for designating a specialized hospital for urologic diseases.Methods: Eight participants experts in urology medicine and specialized hospital system were divided into four groups. Following the semi-structured guidelines, an in-depth interview was conducted twice and a focus group discussion was conducted three times. All the interviews were transcribed verbatim and analyzed.Results: The majority of participants predicted that there would be demand for specialized hospitals for urologic diseases. The criteria of designating a specialized hospital, such as the number of hospital beds and quality of health care, have to be modified in consideration of the specificity of urology. The introduction of a specialized hospital would improve the healthcare delivery system, positively affecting hospitals and patients. Furthermore, government support is essential for the maintenance of specialized hospital systems as urology hospitals experience difficulties in generating profits.Conclusion: This study is expected to be used as base data for introducing and operating a specialized hospital for urologic diseases. In addition, it is expected that the methodology and results of this study would encourage follow-up studies on specialized hospitals and provide guidelines to evaluate the effectiveness of such hospitals in other medical fields.

Disaggregating Asian Race Reveals COVID-19 Disparities Among Asian American Patients at New York City’s Public Hospital System

Objectives: Data on the health burden of COVID-19 among Asian American people of various ethnic subgroups remain limited. We examined COVID-19 outcomes of people of various Asian ethnic subgroups and other racial and ethnic groups in an urban safety net hospital system. Methods: We conducted a retrospective analysis of 85 328 adults aged ≥18 tested for COVID-19 at New York City’s public hospital system from March 1 through May 31, 2020. We examined COVID-19 positivity, hospitalization, and mortality, as well as demographic characteristics and comorbidities known to worsen COVID-19 outcomes. We conducted adjusted multivariable regression analyses examining racial and ethnic disparities in mortality. Results: Of 9971 Asian patients (11.7% of patients overall), 48.2% were South Asian, 22.2% were Chinese, and 29.6% were in other Asian ethnic groups. South Asian patients had the highest rates of COVID-19 positivity (30.8%) and hospitalization (51.6%) among Asian patients, second overall only to Hispanic (32.1% and 45.8%, respectively) and non-Hispanic Black (27.5% and 57.5%, respectively) patients. Chinese patients had a mortality rate of 35.7%, highest of all racial and ethnic groups. After adjusting for demographic characteristics and comorbidities, only Chinese patients had significantly higher odds of mortality than non-Hispanic White patients (odds ratio = 1.44; 95% CI, 1.04-2.01). Conclusions: Asian American people, particularly those of South Asian and Chinese descent, bear a substantial and disproportionate health burden of COVID-19. These findings underscore the need for improved data collection and reporting and public health efforts to mitigate disparities in COVID-19 morbidity and mortality among these groups.

Elevated Blood Pressure in Hospitalized Children Predicts True Elevated Blood Pressure Outpatient

OBJECTIVES: To determine if elevated blood pressure (EBP) in hospitalized children accurately predicts EBP outpatient. METHODS: A multicenter retrospective chart review was conducted at a large hospital system in Northeastern United States. Mean blood pressures during hospitalizations were classified as elevated or not elevated, by using the American Academy of Pediatrics (AAP) 2017 parameters. Mean blood pressure was then compared with each patient’s mean blood pressure measured 3 times postdischarge. The data were analyzed to determine if inpatient EBP is an accurate predictor of outpatient EBP. RESULTS: Of 5367 hospitalized children, 656 (12.2%) had EBP inpatient. Inpatient EBP was highly predictive of outpatient EBP, with a positive predictive value of 96% and negative predictive value of 98%. CONCLUSIONS: Diagnosing hospitalized children with EBP, as defined by the AAP 2017 guidelines, accurately predicts true EBP outpatient.

Post-Mastectomy Patients in an Urban Safety-Net Hospital: How Do Safety-Net Hospital Breast Reconstruction Rates Compare to National Breast Reconstruction Rates?

Background Breast reconstruction (BR) has documented psychological benefits following mastectomy. Yet, racial/ethnic minority groups have lower reported rates of BR. We sought to evaluate the rate, type, and outcome of BR in a racially and ethnically diverse population within a safety-net hospital system. Methods All patients who underwent mastectomy between October 2015 and July 2019 at Harbor-UCLA Medical Center were retrospectively examined. Rates and type of BR were analyzed according to patient characteristics (race/ethnicity, age, and body mass index), smoking status, cancer stage, and presence of diabetes mellitus. Breast reconstruction outcomes were also assessed. Results Of the 259 patients that underwent mastectomy, 87 (33.6%) received BR. Immediate BR was performed in 79 (30.5%) patients and delayed BR in 8 (3.1%). Of the 79 patients with immediate BR, 58 (73.4%) received implant-based BR and 21 (26.5%) autologous tissue. The BR failure rate was 10%, all implant-based. Increasing age and smoking negatively impacted BR rates. Black ( P =.331) and Hispanic ( P =.132) ethnicity were not independent predictors of decreased breast reconstruction. Conclusion This study demonstrated that the rate, type, and quality of BR in this integrated safety-net hospital within a diverse population are comparable to national rates. When made available, historically underrepresented minority patients of Black and Hispanic ethnicity utilize BR.