Improving efficiency and value in health care: introduction

Comparing South Dakota Pharmacist Perspectives of Pharmacy Services in Rural versus Urban settings

Background

Access to healthcare services is a major barrier to residents of the rural state of South Dakota. As a highly accessible member of the healthcare team, outpatient pharmacists can play a key role in a patient's healthcare journey. There is a need to identify the unique barriers and facilitators pharmacists in both rural and urban areas face to maximize the impact of their role.

Objective

The objective of this work was to compare perceptions of rural and urban pharmacists regarding the facilitators and barriers to providing patient care in South Dakota.

Methods

This qualitative project highlights results from interviews and focus group sessions with a convenience sample of South Dakota pharmacists. Participants were recruited using a referral word-of-mouth system, contracts with healthcare market research agencies, newspaper advertisements, and posters displayed in public locations in South Dakota. Practice location was characterized as rural or urban based on United States Department of Agriculture definitions. Findings from interviews and focus group sessions were coded and analyzed using content analysis by two student researchers.

Results

Participants included 12 rural-practicing and 21 urban-practicing pharmacists in South Dakota. In both rural and urban areas, key barriers included communication with providers (50% rural; 50% urban), lack of electronic health record access (25% rural; 14% urban), not enough staff (22% rural; 20% urban), and patient misunderstanding the scope of pharmacy (22% rural; 40% urban). Barriers specific to rural areas included time to provide services (22%), having smaller facilities (27%) and provider hesitation regarding collaborative practice agreements (29%). There were no urban-specific barriers. Facilitators specific to urban areas included frequent communication with patients (6.1%) and good quality support staff (9.1%). There were no rural-specific facilitators.

Conclusions

Next steps include increasing awareness of pharmacy-based patient care services, researching further to identify the extent to which facilitators and barriers influence the ability to initiate and sustain pharmacy services in rural and urban areas, and providing support to pharmacies to overcome barriers and leverage facilitators.

Comparison of Full-Time Equivalent and Clinic Time Labor Input Measures in Productivity Metrics

GOAL

A lack of improvement in productivity in recent years may be the result of suboptimal measurement of productivity. Hospitals and clinics benefit from external benchmarks that allow assessment of clinical productivity. Work relative value units have long served as a common currency for this purpose. Productivity is determined by comparing work relative value units to full-time equivalents (FTEs), but FTEs do not have a universal or standardized definition, which could cause problems. We propose a new clinical labor input measure-"clinic time"-as a substitute for using the reported measure of FTEs.

METHODS

In this observational validation study, we used data from a cluster randomized trial to compare FTE with clinic time. We compared these two productivity measures graphically. For validation, we estimated two separate ordinary least squares (OLS) regression models. To validate and simultaneously adjust for endogeneity, we used instrumental variables (IV) regression with the proportion of days in a pay period that were federal holidays as an instrument. We used productivity data collected between 2018 and 2020 from Veterans Health Administration (VA) cardiology and orthopedics providers as part of a 2-year cluster randomized trial of medical scribes mandated by the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act of 2018.

PRINCIPAL FINDINGS

Our cohort included 654 unique providers. For both productivity variables, the values for patients per clinic day were consistently higher than those for patients per day per FTE. To validate these measures, we estimated separate OLS and IV regression models, predicting wait times from the two productivity measures. The slopes from the two productivity measures were positive and small in magnitude with OLS, but negative and large in magnitude with IV regression. The magnitude of the slope for patients per clinic day was much larger than the slope for patients per day per FTE. Current metrics that rely on FTE data may suffer from self-report bias and low reporting frequency. Using clinic time as an alternative is an effective way to mitigate these biases.

PRACTICAL APPLICATIONS

Measuring productivity accurately is essential because provider productivity plays an important role in facilitating clinic operations outcomes. Most importantly, tracking a more valid productivity metric is a concrete, cost-effective management tactic to improve the provision of care in the long term.

Catholic Teaching: A Middle Ground and Guide for End-of-Life Care and Decision-Making and an Antidote for Dying Badly in America

Dying in the United States is characterized as: medicalized, depersonalized, high technology, fragmented with frequent transitions among care settings, burdensome to patients and families, driven by efficiency and effectiveness, and lacking in key areas, for example, access to palliative care and adequate pain and symptom treatment. Patients and families are often left with a choice of two extremes: vitalism or utilitarian pessimism (utilitarianism). The Catholic Church, however, rejects both of these extremes, and Catholic social teaching (CST) at end of life focuses on ordinary-extraordinary treatments/means, a culture of life and human dignity, accompaniment and community, and caring for whole persons through the end of life. The Catholic tradition of ordinary-extraordinary means is helpful to guide complex end-of-life decisions, regardless of one's religious beliefs, and offers a middle ground between vitalism and utilitarianism that can inform end-of-life care and decision-making for all patients in Catholic health care. While it does not provide answers, it offers guidance and enables conversations that are crucial for the dying and their families to make autonomous, informed decisions about end-of-life care. It provides an opportunity for the dying to let the care team, loved ones, and decision-makers know what a life with meaning, purpose, and passion is for them-and how they want to live and die. This article will summarize the problem, describe end-of-life Catholic teaching, and discuss how it offers a middle-ground. Arguments for and against vitalism and utilitarianism will be explored, including a discussion of CST's response to those receiving care in Catholic health care facilities who are outside the Catholic tradition and do not believe in the teaching. The last section describes a model of collaborative partnership where local parishes and Catholic health care come together to tackle the challenges of caring for and ministering to the seriously ill and those facing death.

Day Admission Surgery Program in a Prospective Payment System: What Are the Financial Incentives?

Background

Day admission surgery (DAS) is meant to provide a better in-hospital experience for patients and to save costs by reducing the length of stay. However, in a prospective payment system, it may also reduce the reimbursement amount, leading to unintended incentives for hospitals.

Methods

Over a 4-month period in 2021 and based on predefined clinical and logistic criteria, patients from different surgical sub-specialties were identified to follow the institutional DAS program. Revenue-analysis was performed, considering the Swiss diagnosis-related group (SwissDRG) prospective payment policy. Revenue with DAS program was compared to revenue if patients were admitted the day prior surgery (No DAS) using nonparametric pooled bootstrap t-test. All other costs considered identical, an estimation of the average cost spared due to the avoidance of pre-operative hospitalization in the DAS setting was carried out using a micro-costing approach.

Results

Overall, 105 inpatients underwent DAS over the study period, totaling a revenue of CHF 1 209 840. Among them, 25 patients (24%) were low outliers due to the day spared from the DAS program and triggering a mean (SD) financial discount of Swiss Francs (CHF) 4192 (2835), yielding a total amount of CHF 105 435. DAS revealed a mean revenue of CHF 7320 (656), compared to CHF 11 510 (1108) if patients were admitted the day before surgery (No DAS, P = .007).

Conclusion

In a PPS, anticipation of financial penalties when implementing a DAS for all-comers is key to prevent an imbalance of the hospital equation if no financial criteria are used to select eligible patients. Promptly revising workflow to maintain constant fixed costs for a greater number of patients may be a valuable hedging strategy.

Patient engagement as a core element of translating clinical evidence into practice- application of the COM-B model behaviour change model

BACKGROUND

The results of rehabilitation trials are often not fully attained when the intervention is implemented beyond the initial trial. One of the key reasons is that a patients' ability and/or capacity to take part in their own healthcare is not considered in the trial design yet has significant impact on the outcomes during the implementation phase.

BODY OF TEXT

We propose a shift from a therapist-focus to patient-focus in trial design, through addressing patient engagement as a core consideration in trials. We argue that engaging patients in any rehabilitation program is a process of behavioural change. Exercise prescription is used as an example to illustrate how the Behaviour Change Wheel can be applied to analyse barriers and facilitators associated with patients' capabilities, opportunities and motivations in integrating trial interventions into their daily life. We propose a framework to assist in this shift.

CONCLUSION

A core part of implementing rehabilitation interventions at the primary care level requires patient engagement. Related aspects of interventions should be identified and assessed using the COM-B model at the outset of trial design to ensure that the results are realistic, meaningful and transferable, so as to enable real impact.

Nursing Workforce Optimization Study: A Multi-method Evaluation and Process Improvement Intervention for HIV Service Delivery in Tanzania and Zambia

ABSTRACT

Nurses are often suboptimally used in HIV care, due to misalignment of training and practice, workflow inefficiencies, and management challenges. We sought to understand nursing workforce capacity and support implementation of process improvement strategies to improve efficiency of HIV service delivery in Tanzania and Zambia. We conducted time and motion observations and task analyses at 16 facilities followed by process improvement workshops. On average, each nurse cared for 45 clients per day in Tanzania and 29 in Zambia. Administrative tasks and documentation occupied large proportions of nurse time. Self-reported competency was low at baseline and higher at follow-up for identifying and managing treatment failure and prescribing antiretroviral therapy. After workshops, facilities changed care processes, provided additional training and mentorship, and changed staffing and supervision. Efficiency outcomes were stable despite staffing increases. Collaborative approaches to use workforce data to engage providers in improvement strategies can support roll-out of nurse-managed HIV treatment.

Identifying modifiable and nonmodifiable cost drivers of ambulatory rotator cuff repair: a machine learning analysis

INTRODUCTION

Implementing novel tools that identify contributors to the cost of orthopedic procedures can help hospitals maximize efficiency, minimize waste, improve surgical decision-making, and practice value-based care. The purpose of this study was to develop and internally validate a machine learning algorithm to identify key drivers of total charges after ambulatory arthroscopic rotator cuff repair and compare its performance with a state-of-the-art statistical learning model.

METHODS

A retrospective review of the New York State Ambulatory Surgery and Services Database was performed to identify patients who underwent elective outpatient rotator cuff repair (RCR) from 2015 to 2016. Initial models were constructed using patient characteristics (age, gender, insurance status, patient income, Elixhauser Comorbidity Index) as well as intraoperative variables (concomitant procedures and services, operative time). These were subsequently entered into 5 separate machine learning algorithms and a generalized additive model using natural splines. Global variable importance and partial dependence curves were constructed to identify the greatest contributors to cost.

RESULTS

A total of 33,976 patients undergoing ambulatory RCR were included. Median total charges after ambulatory RCR were $16,017 (interquartile range: $11,009-$22,510). The ensemble model outperformed the generalized additive model and demonstrated the best performance on internal validation (root mean squared error: $7112, 95% confidence interval: 7036-7188; logarithmic root mean squared error: 0.354, 95% confidence interval: 0.336-0.373, R²: 0.53), and identified major drivers of total charges after RCR as increasing operating room time, patient income level, number of anchors used, use of local infiltration anesthesia/peripheral nerve blocks, non-White race/ethnicity, and concurrent distal clavicle excision. The model was integrated into a web-based open-access application capable of providing individual predictions and explanations on a case-by-case basis.

CONCLUSION

This study developed an ensemble supervised machine learning algorithm that outperformed a sophisticated statistical learning model in predicting total charges after ambulatory RCR. Important contributors to total charges included operating room time, duration of care, number of anchors used, type of anesthesia, concomitant distal clavicle excision, community characteristics, and patient demographic factors. Generation of a patient-specific payment schedule based on the Agency for Healthcare Research and Quality risk of mortality highlighted the financial risk assumed by physicians in flat episodic reimbursement schedules given variable patient comorbidities and the importance of an accurate prediction algorithm to appropriately reward high-value care at low costs.

Determining the Value-Efficiency of Respiratory Care

In order to determine, document, and communicate the value of respiratory therapists performing respiratory care procedures, the respiratory care profession needs to position itself to capture and report both time and value standards that can be applied in allocating respiratory care resources. To do this, we propose a new metric called value-efficiency. If we wish to use value-efficiency as a metric to justify respiratory care activities and support labor budgets, there are three key considerations: (1) What value does respiratory care add to the health care organization? (2) Are the interventions provided necessary and of clinical value? (3) What is the value of the respiratory therapist in the delivery of these services? Significant challenges are facing the respiratory care profession and a focus on value-efficiency is a direction the profession must pursue. This approach is a practical response to the increasing demands of payers, administrators, consultants, and patients.

Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis

For patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from the 2010 Behavioral Risk Factor Surveillance System. 1112 US counties were analyzed. Cluster analysis was used to place counties into three similar groups in terms of economic wellbeing and population characteristics. Group 1 consisted of metropolitan counties with prosperous or comfortable economic levels. Group 2 mostly consisted of non-metropolitan areas between distress and mid-tier levels, while Group 3 were mostly prosperous or comfortable counties in metropolitan areas. We used data enveopement analysis to assess efficiencies within each group. The majority of counties had modest efficiency in providing diabetes preventive care; 36 counties (57.1%), 345 counties (61.1%), and 263 counties (54.3%) were inefficient (efficiency scores < 1) in Group 1, Group 2, and Group 3, respectively. For inefficient counties, foot and eye exams were often identified as sources of inefficiency. Available health professionals in some counties were not fully utilized to provide diabetes preventive care. Identifying benchmarking targets from counties with similar resources can help counties and policy makers develop actionable strategies to improve performance.

The Impact of Public Reporting Schemes and Market Competition on Hospital Efficiency

In the wake of growing attempts to assess the validity of public reporting, much research has examined the effectiveness of public reporting regarding cost or quality of care. However, relatively little is known about whether transparency through public reporting significantly influences hospital efficiency despite its emerging expectations for providing value-based care. This study aims to identify the dynamics that transparency brought to the healthcare market regarding hospital technical efficiency, taking the role of competition into account. We compare the two public reporting schemes, All-Payer Claims Database (APCD) and Hospital Compare. Employing Data Envelopment Analysis (DEA) and a cross-sectional time-series Tobit regression analysis, we found that APCD is negatively associated with hospital technical efficiency, while hospitals facing less competition responded significantly to increasingly transparent information by enhancing their efficiency relative to hospitals in more competitive markets. We recommend that policymakers take market mechanisms into consideration jointly with the introduction of public reporting schemes in order to produce the best outcomes in healthcare.

Machine learning ensemble models predict total charges and drivers of cost for transsphenoidal surgery for pituitary tumor

OBJECTIVE

Efficient allocation of resources in the healthcare system enables providers to care for more and needier patients. Identifying drivers of total charges for transsphenoidal surgery (TSS) for pituitary tumors, which are poorly understood, represents an opportunity for neurosurgeons to reduce waste and provide higher-quality care for their patients. In this study the authors used a large, national database to build machine learning (ML) ensembles that directly predict total charges in this patient population. They then interrogated the ensembles to identify variables that predict high charges.

METHODS

The authors created a training data set of 15,487 patients who underwent TSS between 2002 and 2011 and were registered in the National Inpatient Sample. Thirty-two ML algorithms were trained to predict total charges from 71 collected variables, and the most predictive algorithms combined to form an ensemble model. The model was internally and externally validated to demonstrate generalizability. Permutation importance and partial dependence analyses were performed to identify the strongest drivers of total charges. Given the overwhelming influence of length of stay (LOS), a second ensemble excluding LOS as a predictor was built to identify additional drivers of total charges.

RESULTS

An ensemble model comprising 3 gradient boosted tree classifiers best predicted total charges (root mean square logarithmic error = 0.446; 95% CI 0.439-0.453; holdout = 0.455). LOS was by far the strongest predictor of total charges, increasing total predicted charges by approximately $5000 per day.In the absence of LOS, the strongest predictors of total charges were admission type, hospital region, race, any postoperative complication, and hospital ownership type.

CONCLUSIONS

ML ensembles predict total charges for TSS with good fidelity. The authors identified extended LOS, nonelective admission type, non-Southern hospital region, minority race, postoperative complication, and private investor hospital ownership as drivers of total charges and potential targets for cost-lowering interventions.

Development of data integration and visualization tools for the Department of Radiology to display operational and strategic metrics

Visualizing process metrics can help identify targets for improvement initiatives. Dashboards and scorecards are tools to visualize important metrics in an easily interpretable manner. We describe the development of two visualization systems: a dashboard to provide real-time situational awareness to frontline coordinators, and a scorecard to display aggregate monthly performance metrics for strategic process improvement efforts. Both systems were designed by a multidisciplinary team of physicians, allied health staff, engineers and information technology specialists. We describe the process of defining important metrics, gathering and cleaning data, and designing the visualization interfaces. We also describe some improvement initiatives that stemmed. These systems were implemented in our hospital and improved the availability of data to our staff and leadership, making performance gaps visible and generating new targets for quality improvement projects.

Home oxygen therapy criteria, guidelines and protocols for hypoxia management in pediatric patients with acute bronchiolitis: a scoping review protocol

REVIEW OBJECTIVE/QUESTION

The objectives of this scoping review are: to explore existing literature related to discharge criteria, guidelines and protocols from hospitals or clinics that are being implemented for home oxygen therapy (HOT) in the management of hypoxia in pediatric patients with acute bronchiolitis, examine and conceptually map the evidence, and identify any gaps in the literature.The question of this review is: what discharge criteria, guidelines, and protocols are used for HOT in the management of hypoxia in pediatric patients with acute bronchiolitis?

A real-world, multi-site, observational study of infusion time and treatment satisfaction with rheumatoid arthritis patients treated with intravenous golimumab or infliximab

OBJECTIVES

To assess real-world infusion times for golimumab (GLM-IV) and infliximab (IFX) for rheumatoid arthritis (RA) patients and factors associated with treatment satisfaction.

METHODS

An observational study assessed infusion time including: clinic visit duration, RA medication preparation and infusion time, and infusion process time. Satisfaction was assessed by a modified Treatment Satisfaction Questionnaire for Medication (patient) and study-specific questionnaires (patient and clinic personnel). Comparative statistical testing for patient data utilized analysis of variance for continuous measures, and Fisher's exact or Chi-square test for categorical measures. Multivariate analysis was performed for the primary time endpoints and patient satisfaction.

RESULTS

One hundred and fifty patients were enrolled from six US sites (72 GLM-IV, 78 IFX). The majority of patients were female (80.0%) and Caucasian (88.7%). GLM-IV required fewer vials per infusion (3.7) compared to IFX (4.9; p = .0001). Clinic visit duration (minutes) was shorter for GLM-IV (65.1) compared to IFX (153.1; p < .0001), as was total infusion time for RA medication (32.8 GLM-IV, 119.5 IFX; p < .0001) and infusion process times (45.8 GLM-IV, 134.1 IFX; p < .0001). Patients treated with GLM-IV reported higher satisfaction ratings with infusion time (p < .0001) and total visit time (p = .0003). Clinic personnel reported higher satisfaction with GLM-IV than IFX specific to medication preparation time, ease of mixing RA medication, frequency of patients requiring pre-medication, and infusion time.

LIMITATIONS

Findings may not be representative of care delivery for all RA infusion practices or RA patients.

CONCLUSIONS

Shorter overall clinic visit duration, infusion process, and RA medication infusion times were observed for GLM-IV compared to IFX. A shorter duration in infusion time was associated with higher patient and clinic personnel satisfaction ratings.

Improving hospital efficiency: a process model of organizational change commitments

Improving hospital efficiency is a critical goal for managers and policy makers. We draw on participant observation of the perioperative coaching program in seven Ontario hospitals to develop knowledge of the process by which the content of change initiatives to increase hospital efficiency is defined. The coaching program was a change initiative involving the use of external facilitators with the goal of increasing perioperative efficiency. Focusing on the role of subjective understandings in shaping initiatives to improve efficiency, we show that physicians, nurses, administrators, and external facilitators all have differing frames of the problems that limit efficiency, and propose different changes that could enhance efficiency. Dynamics of strategic and contested framing ultimately shaped hospital change commitments. We build on work identifying factors that enhance the success of change efforts to improve hospital efficiency, highlighting the importance of subjective understandings and the politics of meaning-making in defining what hospitals change.