Reduce Health Care Spending Research Articles

The crowding-out effect of physical fitness activities on medical expenditure in the aged group

IntroductionChina is facing the challenge of “deep aging”, and promoting healthy aging has become a key research topic. Both medical care and physical exercise are important for health, but while medical services focus on treating illness, physical fitness activities focus on prevention, making them a more effective approach for promoting healthy aging.MethodsThis study uses data from the China Health and Retirement Longitudinal Study (CHARLS), focusing on individuals aged 60 to 80, to investigate the relationship between physical fitness activities and medical expenditures. A Tobit model was employed to analyze the data.Results(1) Active participation in physical fitness activities significantly improves the health of older adults, making physical activity an essential pathway to achieve healthy aging. (2) Participation in sports and fitness activities leads to a crowding-out effect on medical expenditures, significantly reducing healthcare costs for participants. (3) High-intensity physical activities are most suitable for individuals aged 60-65, moderate-intensity activities for those aged 66-70, and low-intensity activities for those aged 71–80.Discussion(1) Policies should focus on raising awareness of physical fitness benefits among older adults, encouraging regular physical activity to improve health and reduce medical costs. (2) A shift from treatment to prevention in health management is needed, promoting exercise as a cost-effective way to reduce healthcare spending. (3) Age-specific fitness guidelines should be developed to provide tailored exercise recommendations for different older aldult age groups.

Harnessing AI for Early Detection of Cardiovascular Diseases: Insights from Predictive Models Using Patient Data

Cardiovascular diseases (CVDs) continue to be the leading cause of death in the world, taking millions of lives every year. Early detection and treatment of these diseases are critical in preventing deaths and illnesses. Currently, traditional diagnostic methods like routine clinical examinations and other standard risk assessment tools fail to detect CVDs in early on set stages, posing numerous limitations for preventable interventions. This work explores incorporating artificial intelligence (AI) to catalyze early CVD diagnosis using AI models for patient data including electrocardiogram (ECG), wearable-generated metrics, and medical history. The research showcased the ability of AI to predict with high accuracy when CVD would start using machine learning methods such as Random Forest, Support Vector Machines (SVM) and Neural Networks. The powerful AI models were carefully constructed by training and testing them on an extensive dataset that contained measurements of diverse patient characteristics including many other readouts related to cardiovascular risk. Compared to traditional methods, the models outperformed, and Neural Networks had an accuracy of 92% in identifying high-risk patients. A hallmark of this work is that AI identifies subtle patterns and relationships within patient data ones that conventional approaches might not find. These AI-based predictive models might soon become part of the routine clinical evaluation for cardiovascular disease, providing a personalized and early health intervention to empower your cardiovascular care. Timely identification of these high-risk patients could allow for targeted interventions, leading to improved health and reductions in healthcare spending. Despite the promising potential that AI holds in healthcare, it also has brought up issues of data privacy, ethical use and the need for extensive clinical validation. This paper suggests a predictive strategy could be implemented using AI algorithms to lead early detection and management of CVD, which opens an opportunity for individualized care accompanied by big data era. Future studies should focus on improving such models with more diverse and larger datasets as well as overcoming logistical difficulties encountered in integrating AI into clinical workflows.

Home-based care and center-based care: From being alternatives to being synergistic. Optimization models to support flexible care delivery

The delivery of health care services, especially for patients with chronic conditions and/or requiring cyclical treatment, can be accomplished by resorting to two organizational models: center-based care and home-based care. In developed countries, government pressure to reduce healthcare spending and the COVID-19 pandemic have led to the spread of home-based care. This type of care is proven to outperform the center-based one in terms of cost-effectiveness, patient satisfaction, and adherence to treatment guidelines. However, even for treatments usually suitable to be delivered at home, the patient's health status or other constraints may make it inappropriate to deliver service at the patient's home. This calls for a new organizational model, referred to as flexible care, where home-based and center-based care are not seen as mutually exclusive models but as options that can be activated according to the patient's and provider's needs. This paper presents a novel network-based deterministic optimization model and two matheuristics to address a scheduling problem typically faced by providers adopting a flexible care model. The model considers a provider relying on a treatment room with a fixed number of medical chairs, a fleet of vehicles, and a team of operators. It allows for determining on which days of the planning horizon, in which setting (home or center), and by which operator each patient will be treated. The model takes into account patients’ preferences and considers two objective functions: minimizing provider costs and patients’ travel time. In addition, we propose a two-stage mixed-integer stochastic programming model with recourse actions. This model allows incorporating the uncertainty due to the occurrence of adverse events. Adverse events are sudden changes in the patient's condition randomly happening at a specific point in the planning horizon. These events render the patient unsuitable for home care and require them to be visited at the center from that moment onward. The models have been inspired by a real case and tested on multiple random instances.

Policies and cost analyses of voluntary assisted dying (VAD) laws – a mapping review & analysis

ObjectivesTo investigate the current literature on healthcare policies and cost analyses around international Voluntary Assisted Dying (VAD) laws. The study design is a mapping literature review following Preferred-Reporting-Items-for-Systematic-Reviews-and-Meta-Analyses (PRISMA) guidelines.MethodsOriginal research articles published between January 1990 to March 2023, investigating the financial cost and healthcare budget effect of VAD laws internationally. Citations were screened for relevance and eligibility, and any non-full-text research that did not explore cost analysis was excluded. The following data sources were screened: MEDLINE, PubMed, EMBASE, CINAHL and any relevant international health authority annual reports were also reviewed.ResultsOf the 2790 screened articles, eight studies met the inclusion criteria and three were included in the mapping review. The reviewed studies included prospective studies, two Canadian and one US. Only one of the Canadian studies provided a cost analysis using data from current VAD laws. All three studies showed VAD laws would reduce healthcare spending, with the US approximating $627million in 1995. Canada approximating $17.1 to $77.1million in 2017 and $86.9 to $149.0million in 2021, overall, leading to an average percentage reduction in costs of approximately 87% compared to original costs of end-of-life care.ConclusionThis review identifies a scarcity in cost-analysis literature and provides a summary of the latest international VAD laws, from which a potential cost reduction is apparent. The absence of retrospectively collated financial VAD data highlights a need for future research to inform policymakers of the economic factors affecting current policies with a need for annual fiscal reports and to optimise future legislative frameworks internationally.

Estimating the Potential Health Care Cost-Savings from a Flax-Based Treatment for Hypertension.

Hypertension contributes to the increase in health care spending in Canada through two primary mechanisms. First, it directly increases costs, as individuals with hypertension require medical care to manage the condition. Second, it indirectly raises expenses by serving as a risk factor for numerous chronic diseases, leading to increased health care utilization among those affected. Therefore, reducing hypertension prevalence could alleviate its resulting strain on the Canadian health care system. Clinical trials have demonstrated that daily flaxseed consumption effectively lowers both systolic and diastolic blood pressure. This study employs a four-step cost-of-illness analysis to estimate the potential health care cost-savings from a flaxseed-based treatment for hypertension. The analysis begins by assessing the proportion of individuals with hypertension likely to adopt the flaxseed regimen. It then evaluates the impact of flaxseed consumption on systolic and diastolic blood pressure. Next, data from the Canadian Health Measures Survey, Cycles 5 and 6, are used to estimate the prevalence of hypertension and the expected reduction in prevalence due to the flaxseed treatment. Finally, the potential reduction in health care spending is calculated. To incorporate uncertainty, partial sensitivity analysis and Monte Carlo simulations were utilized, varying the intake success rate and other model parameters, respectively. The most conservative estimate suggests a potential health care cost-savings of CAD 96,284,344 in Canada for the year 2020.

Cost Analysis of Oral Phenazopyridine vs Intravesical Lidocaine for Preprocedural Analgesia for Intradetrusor OnabotulinumtoxinA Injections.

Office administration of intradetrusor onabotulinumtoxinA is commonly used to treat overactive bladder. For preprocedure analgesia, either 50 mL 2% intravesical lidocaine instillation for 20 to 30 minutes or 200 mg oral phenazopyridine can be used. Phenazopyridine is associated with shorter appointment times and is noninferior to lidocaine for pain control in this setting. We performed a cost analysis of phenazopyridine vs lidocaine for analgesia before office intradetrusor onabotulinumtoxinA injection for the treatment of idiopathic overactive bladder. A health care sector-perspective cost analysis was performed. The following assumptions were made: (1) similar efficacy of each medication in providing adequate analgesia, (2) similar physician ease of performing the procedure with either analgesic, and (3) similar patient satisfaction with either analgesic. Average cost of medications, adverse reactions, nursing tasks, and office visit time were found in publicly available data. Sensitivity analyses were performed using TreeAge Pro 2021, R1 software. Phenazopyridine is less costly compared to lidocaine per visit for office intradetrusor onabotulinumtoxinA injection ($827 vs $925). A difference of $98 per procedure provides a total annual cost savings of over $24 million if all procedures are performed with phenazopyridine instead of lidocaine. Sensitivity analysis showed that phenazopyridine remained less costly under most circumstances, and threshold analysis provided exact circumstances under which phenazopyridine is no longer cost saving. Phenazopyridine provides cost savings compared to lidocaine for analgesia before office intradetrusor onabotulinumtoxinA injection for the treatment of idiopathic overactive bladder. If adopted by providers nationwide, phenazopyridine may reduce health care spending and minimize office visit time while maintaining patient pain control and satisfaction.

Policy steps in the federal budget and health care to achieve sustainable fiscal conditions in the U.S.

Health care spending has grown significantly in the US, and is a major cause of rising federal deficits. Official projections find that low economic growth and aging of the population will lead to a modest continuation of this situation, but they miss that labor shortages have a particular impact on inefficient sectors, raising the cost of health care. We develop a unified macroeconomic model to address this shortcoming, with key variables—particularly in the health care sector and interest rates—endogenously determined. We find that the current fiscal trajectory of the US is unsustainable, with future deficits and debt well beyond both historical norms and official projections. Policy steps, focusing on reducing health care spending, increasing capital, and fiscal retrenchment, can lead to sustainable fiscal conditions and raise living standards that will otherwise fall.

Home Heart Hospital Associated With Reduced Hospitalizations and Costs Among High-Cost Patients With Cardiovascular Disease.

There is no widely accepted care model for managing high-need, high-cost (HNHC) patients. We hypothesized that a Home Heart Hospital (H3), which provides longitudinal, hospital-level at-home care, would improve care quality and reduce costs for HNHC patients with cardiovascular disease (CVD). To evaluate associations between enrollment in H3, which provides longitudinal, hospital-level at-home care, care quality, and costs for HNHC patients with CVD. This retrospective within-subject cohort study used insurance claims and electronic health records data to evaluate unadjusted and adjusted annualized hospitalization rates, total costs of care, part A costs, and mortality rates before, during, and following H3. Ninety-four patients were enrolled in H3 between February 2019 and October 2021. Patients' mean age was 75 years and 50% were female. Common comorbidities included congestive heart failure (50%), atrial fibrillation (37%), coronary artery disease (44%). Relative to pre-enrollment, enrollment in H3 was associated with significant reductions in annualized hospitalization rates (absolute reduction (AR): 2.4 hospitalizations/year, 95% confidence interval [95% CI]: -0.8, -4.0; p < 0.001; total costs of care (AR: -$56 990, 95% CI: -$105 170, -$8810; p < 0.05; and part A costs (AR: -$78 210, 95% CI: -$114 770, -$41 640; p < 0.001). Annualized post-H3 total costs and part A costs were significantly lower than pre-enrollment costs (total costs of care: -$113 510, 95% CI: -$151 340, -$65 320; p < 0.001; part A costs: -$84 480, 95% CI: -$121 040, -$47 920; p < 0.001). Longitudinal home-based care models hold promise for improving quality and reducing healthcare spending for HNHC patients with CVD.

Advancing Health Equity Through Issue Spotting in Primary Care: A Case for Medical-legal Partnerships

Social and legal determinants of health have been shown to have substantial impacts on the health outcomes of individual patients and the population as a whole. This case report highlights the importance of screening for social needs as well as having appropriate interventions to address their impact. One intervention is medical-legal partnerships. The case presented describes the use of social screening tools and medical-legal partnerships to address health concerns, reduce health care spending, and promote optimal health outcomes. The case provided is a patient who was evaluated for bilateral lower extremity swelling in a primary care setting.

Making a Case for Whole Person Health.

Background: Our conventional approach to health care tends to separate patients' health by body system, treating each independently and "efficiently"-e.g., minimal time with a provider, reliance on medications, and little investment to support behavioral and lifestyle improvements. Meanwhile, the United States has the most expensive health care in the world, with some of the worse outcomes. Purpose In this paper, we make the case for transforming health care from a disease-centric approach to a "whole person" model. Research Design: We provide detailed health and health care utilization assumptions for a hypothetical patient, Mrs. M, over her life from age 40 to 80 years under 2 care scenarios: the continuation of conventional care (Version A) and a whole person care approach (Version B). Analysis We developed a set of health care utilization assumptions for each scenario, applied 2023 U.S. dollar (USD) resource prices, and estimated cumulative total health care costs. The price and the health care utilization assumptions for the conventional care scenario were validated using Medical Expenditures Panel Survey (MEPS) data. Results: At age 80, with conventional care, we find Mrs. M increasingly frail and living in a skilled nursing facility, with total cumulative health care costs of $353,155. With whole person care, we find her active and generally healthy at age 80, with total cumulative health care costs of $52,425. Conclusions: Although based on an "imagined" case, the 2 versions of Mrs. M's history illustrate how an investment beginning in early middle age to support a healthy diet, physical activity, and stress management can plausibly lead to improved health and well-being, as well as reduced health care spending.

Patients, practice, and price transparency: The impact of disclosing healthcare costs on consumer decision-making

The introduction of price transparency mandates by the Center for Medicare and Medicaid Services (CMS) aimed to reduce healthcare spending by promoting price comparison and the selection of lower-cost providers. In this brief communication, we will discuss how consumer interests in price comparison are influenced and how price transparency is expected to impact consumer decision-making with specific examples for imaging as a shoppable service.

Accountable Care Organization Attribution and Post-Acute Skilled Nursing Facility Outcomes for People Living With Dementia

ObjectivesUnder the Accountable Care Organization (ACO) model, reductions in healthcare spending have been achieved by targeting post-acute care, particularly in skilled nursing facilities (SNFs). People with Alzheimer disease and related dementias (ADRD) are frequently discharged to SNF for post-acute care and may be at particular risk for unintended consequences of SNF cost reduction efforts. We examined SNF length of stay (LOS) and outcomes among ACO-attributed and non–ACO-attributed ADRD patients. DesignObservational serial cross-sectional study. Setting and ParticipantsTwenty percent national random sample of fee-for-service Medicare beneficiaries (2013-2017) to identify beneficiaries with a diagnosis of ADRD and with a hospitalization followed by SNF admission (n = 263,676). MethodsOur primary covariate of interest was ACO (n = 66,842) and non-ACO (n = 196,834) attribution. Hospital readmission and death were measured for 3 time periods (<30, 31-90, and 91-180 days) following hospital discharge. We used 2-stage least squares regression to predict LOS as a function of ACO attribution, and patient and facility characteristics. ResultsACO-attributed ADRD patients have shorter SNF LOS than their non-ACO counterparts (31.7 vs 32.8 days; P < .001). Hospital readmission rates for ACO vs non-ACO differed at ≤30 days (13.9% vs 14.6%; P < .001) but were similar at 31-90 days and 91-180 days. No significant difference was observed in mortality post–hospital discharge for ACO vs non-ACO at ≤30 days; however, slightly higher mortality was observed at 31-90 days (8.4% vs 8.8%; P = .002) and 91-180 days (7.6% vs 7.9%; P = .011). No significant association was found between LOS and readmission, with small effects on mortality favoring ACOs in fully adjusted models. Conclusions and ImplicationsBeing an ACO-attributed patient is associated with shorter SNF LOS but is not associated with changes in readmission or mortality after controlling for other factors. Policies that shorten LOS may not have adverse effects on outcomes for people living with dementia.

Research Electronic Data Capture (REDCap) in an outpatient oncology surgery setting to securely email, collect, and manage survey data.

Nursing interventions in the post-operative time period including psychological and emotional support, adverse event education, and instructions for follow-up care contribute patient satisfaction, safety, and quality of life. However, the time spent in the post-anesthesia care unit (PACU) and hospital continues to shorten around the world to reduce health care spending and improve patient outcomes. Nurses conducting research during the important post-operative recovery period need to utilize unique techniques and emerging technologies to contact, recruit and collect data outside of the hospital setting including the Research Electronic Data Capture (REDCap) platform. This paper describes the feasibility and acceptability, facilitators and barriers of the software application, REDCap, to complete a repeated-measures, descriptive correlational study in patients undergoing outpatient breast cancer surgeries. The recruitment, data collection and storage were completed utilizing the secure REDCap Platform. The Institutional Research Board (IRB)-approved study was a repeated-measures, descriptive, correlational study with data collection at three time points. The data points aligned with important transitions and routine visits to improve data collection feasibility and increase relevance to clinical practice. The sample consisted of women diagnosed with breast cancer undergoing breast conserving surgery between August 15 and October 15, 2020. There were 123 potential participants, of which 76 started the surveys and 75 participated (61%) responded and participated in the study on Post-operative Day 1. Fifty-nine participants (78%) completed the surveys on post-operative Day 14. As the frequency of outpatient treatment increases, nurses conducting post-operative research will need to collect the data outside of the hospital setting. Email provides a method of studying new phenomena by recruiting participants, providing information about the study, and collecting results in a non-traditional setting. REDCap provides a method to facilitate nursing research through a securely encrypted integrated process.

Abstract 15443: An Economic Modeling Analysis of an Intensive GDMT Optimization Program in Hospitalized Heart Failure Patients

Background For patients with heart failure with reduced ejection fraction (HFrEF), suboptimal initiation and titration of guideline-directed medical therapy (GDMT) has resulted in preventable loss of life and function. The STRONG-HF trial reinforced this finding by demonstrating substantial reductions in the composite of mortality and morbidity over 6 months among hospitalized heart failure (HF) patients who were randomized to intensive GDMT optimization compared to usual care. Research Question Whether an intensive GDMT optimization program would be cost-effective in HFrEF has yet to explored. Aim We performed an economic analysis of a hypothetical intensive GDMT optimization program over 6 months in patients hospitalized with HFrEF compared to usual care. Methods: Using a 2-state Markov model we evaluated the effect of an intensive GDMT optimization program on patients hospitalized with HFrEF. Two population models were created to simulate this intervention, the first, a “clinical trial” model, based off the participants in the STRONG-HF trial and the second, a “real world” model, based off the Get With The Guidelines-HF Registry of patients admitted with worsening HF. We then modeled the effect of a 6-month intensive GDMT optimization program comprised of a HF physician, clinical pharmacists, and registered nurses. Hazard ratios from the intervention arm of the STRONG-HF trial were applied to both populations models to simulate clinical and financial outcomes of an intensive GDMT optimization program from a United States healthcare perspective with a lifetime time horizon. Results: An intensive GDMT optimization program, resulted in an additional 1.04 and 0.59 life years in the clinical trial and real-world models, respectively. Both models showed the program would be highly cost-effective with incremental cost-effectiveness ratios &lt;$5000 and reductions in healthcare spending due to reduced hospitalizations in the 6 months after discharge. Conclusions: An intensive GDMT optimization program for patients hospitalized with HFrEF would be highly cost-effective and result in substantial gains in clinical outcomes. Clinicians, payers, and policy makers should prioritize creation of such programs.

Outcomes-Based Standardization: A New Approach to An Old Problem

The COVID-19 pandemic has significantly strained care delivery, reduced available resources, and further destabilized health care finances. As health care organizations emerge from a pandemic that has worsened uncontrolled health care spending, while greatly reducing patient volumes and revenue, reactive cost cutting with little regard for the people at the end of those decisions quickly emerged as the standard approach. Historically, limiting cost decisions to product selection was a common, yet minimally effective, strategy for controlling health care spending. In the post-COVID health care environment, where clinical and financial challenges are greater than ever, a new approach to reducing health care spending offers promise. Outcomes-based standardization is an approach that begins with the end in mind, incorporating lean concepts to alleviate redundant or ineffective products and practice, while prioritizing value added activities to catalyze the greatest reduction of harm, time, and money spent. Outcomes-based standardization is a framework for change that balances clinical and financial decisions to ensure high value care across the continuum. This new approach has been employed across the country to help health care organizations reduce health care spending. This article explains what it is, why it works, and how the reader can successfully deploy it across the health care spectrum to achieve better clinical outcomes, alleviate waste, and reduce unnecessary health care spending.

Telehealth medication management and health care spending in a Medicare Accountable Care Organization.

BACKGROUND: Value-based care is an opportunity for medication optimization services to improve medication management and reduce health care spending. The reach of these services may be extended through telehealth. However, as health care systems and payers grapple with the long-term financing of telehealth, real-world assessments are needed to evaluate the potential economic impact of pharmacy-driven telehealth services. OBJECTIVE: To evaluate the impact of a scalable pharmacist-driven telehealth intervention to improve medication management on health care spending for clinically complex patients who were enrolled in a Medicare Next Generation Accountable Care Organization. METHODS: Data for this pretest-posttest nonequivalent group design study came from Medicare claims from 2015 to 2020 and linked pharmacist care activity data derived from the electronic medical record. Patients in the intervention group were identified as those who received the telehealth medication management service. Patients in the control group were offered the service and refused or could not be contacted. The primary outcome was total medical spending over a 6-month period, and impact was assessed using a covariate-adjusted difference-in-difference model. RESULTS: There were 581 patients who received the intervention and 1,765 who served as controls. The telehealth intervention reduced total medical spending by $2,331.85 per patient over the first 6 months of the service ($388.50 per month; P = 0.0261). Across a range of estimates for the cost of service delivery, we find a return on investment of 3.6:1 to 5.2:1. CONCLUSIONS: The $388.50 monthly savings found in this study represent a substantial reduction in health care spending and emphasize the opportunity for telehealth delivery of medication management services to improve value as a part of alternative payment models. DISCLOSURES: This study was funded by the UNC Health Care Department of Pharmacy. Dr Urick has received personal fees from Pharmacy Quality Solutions and Cardinal Health. Drs Peters, Vest, Colmenares, Foushee, and DeFalco are employees of UNC Health Care Department of Pharmacy. Mr Easter received a Co-PI grant from NACDS Foundation to implement Community-based Value Care Initiative (CVCI) in a community pharmacy setting. He also received a grant to Eshelman School of Pharmacy. He has an individual consulting agreement with digital health company Truentity Health to provide go to market strategy for medication management services. He also has stock options from Truentity Health as a component of the consulting agreement. Dr Foushee has a leadership or fiduciary role at Member-At-Large and the Ambulatory Care Academy of the North Carolina Association of Pharmacists. At the time this work was conducted, Dr Urick, Dr Pathak, and Dr Blanchard were all employees of the UNC Eshelman School of Pharmacy.

National Analysis of Health care Related Costs for Health care Workers and Administrators Over the Past Decade: Toward Establishing Sustainable and Cost Effective Health care System.

We aim to investigate the costs associated with growth in the administrators, health care staff, and physicians to provide direction to establish a sustainable and cost-effective U.S. health care system. Data from the U.S. Bureau of Labor Statistics, particularly the Labor Force Statistics from the Current Population Survey, were utilized from 2009 to 2020. Wages and employment of medical and health service managers (administrators), health care practitioners and technical operations (health care staff), and physicians were used to calculate the total cost. Administrator wages have grown similarly to health care staff wages (-4.40 vs -3.01%, P = .454) and physician wages (-4.40 vs -3.29%, P = .672). Additionally, there has been a similar increase in health care staff employment (9.91 vs 14.23%, P = .269) and physician employment (9.91 vs 15.35%, P = .252) compared to administrator employment. Overall, the total growth in administrator cost is similar to the growth in total health care staff cost (6.23 vs 11.80, P = .104) and total physician cost (6.23 vs 13.02%, P = .079). In 2020, physicians had the highest employment growth but the smallest wage increase. Although health care staff experienced a greater percent growth in employment and cost per employee than administrators since 2009, the cost per administrator remains greater than that of health care staff. Understanding differences in wages and costs is essential to reduce health care spending without compromising access, delivery, and quality of health care services.

What Does a Provider Network Do? Evidence from Random Assignment in Medicaid Managed Care

Leveraging the random assignment of over 50,000 Medicaid enrollees in New York, I present causal evidence that narrower networks are a blunt instrument for reducing health care spending. While narrower networks constrain spending, they do so by generating hassle costs that reduce quantity, with modest effects on prices paid to providers. Enrollees assigned to narrower networks use fewer of both needed and unneeded services and are less satisfied with their plans. Using my causal estimates to construct counterfactuals, I identify an alternative assignment policy that reduces spending without harming satisfaction by matching consumers with narrower networks that include their providers. (JEL H51, H75, I13, I18, I38)

Impact of a link worker social prescribing intervention on non-elective admitted patient care costs: A quasi-experimental study

Healthcare systems in many countries are enthusiastically adopting link worker social prescribing interventions that aim to tackle the social determinants of health by linking patients to voluntary and community sector activities and sources of support to address their social needs and improve condition management. Social prescribing interventions aim to improve health and reduce healthcare spending. However, despite the diversion of healthcare budgets to fund social prescribing, we still lack robust evidence for its effectiveness. In this study we evaluate whether participation in a social prescribing intervention reduced non-elective admitted patient care use and costs for 8283 patients aged between 40 and 74 years, with a diagnosis of type 2 diabetes and living in an area of high socioeconomic deprivation in north-east England. Patients were followed for a total of 6 years: 2 years pre-intervention and 4 years post intervention. Exploiting a natural experiment, we used a two-part difference-in-differences regression model to estimate costs conditional on healthcare use. We also estimated intervention effects across several intervention and control groups and sample subgroups. Participation in the intervention resulted in reductions of up to -£77.57 [95% CI: -152.30, -2.84] (for high engagement patients) per patient, per year, in non-elective care costs. Reductions were greater for patients with higher levels of engagement with the intervention. Sub-group analyses showed greater cost reductions for non-White patients, older patients, and patients without additional co-morbidities. Our findings suggest that engagement with a link worker social prescribing intervention may reduce non-elective healthcare spending, perhaps through enabling better condition management that results in fewer avoidable health crises.

Estimating the Lifetime Medical Cost Burden of an Allogeneic Hematopoietic Cell Transplantation Patient and the Value of Addressing the Unmet Need

Background: Allogeneic hematopoietic cell transplantation (allo-HCT) management has evolved, with the emergence of molecular detection assays and therapies to treat post-HCT complications such as graft-versus-host disease (GVHD). However, this approach could increase the economic burden on healthcare systems by only managing complications that occur after the initial transplant. Instead, alternative therapies involving novel engineered donor allografts to replace standard allo-HCT to prevent late complications may potentially improve outcomes and address a greater unmet need. Objective: To estimate (1) the average total lifetime medical costs of an allo-HCT and (2) to estimate the net monetary savings and value associated with reducing late complications. Methods: A short-term decision tree (100-day) and a long-term semi-Markov partitioned survival model were developed that estimated the average per-patient lifetime cost and expected quality-adjusted life years (QALYs) for an allo-HCT patient from a United States healthcare system perspective. The allo-HCT patient population represented an average 53-year-old patient who was undergoing an allo-HCT to treat the three most common indications for allo-HCT: acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), or myelodysplastic syndromes (MDS), distributed as 51%, 21%, and 28%, respectively. Key model clinical inputs included: overall survival, GVHD-free relapse-free survival (GRFS), incidence of both acute and chronic GVHD, relapse of the primary disease, and infections. Following the first 100 days, the patient cohort was assigned to 3 mutually exclusive health states in a semi-Markov partitioned survival model for the remainder of their lifetime: (1) GRFS, (2) progressed and/or GVHD, and (3) death. The projected time spent in each health state was derived from data provided by CIBMTR. Costs pertaining to allo-HCT and associated complications were obtained from the published literature based on medical claims analyses of payments (paid amounts) and were converted into costs using the American Hospital Association (AHA) commercial payment-to-cost ratio. Maintenance therapy costs for FLT3-ITD positive AML and Ph+ ALL cases were also included and were based on published literature, expert opinion, and prescribing information. The key outcomes were direct medical costs and expected QALYs. Cost results were reported as a range based on varying the percent of chronic GVHD patients that remained on treatment after two years (15% or 39%) as derived from the published literature. Scenario analyses were conducted to estimate the potential clinical and economic value that may be realized by improving GRFS outcomes. The scenario analyses to address some of the unmet need were modeled as a doubled improvement in GRFS (69% GRFS in year 1 as opposed to 34%) to estimate changes in allo-HCT-associated costs and survival. A willingness-to-pay threshold of $150,000 was used to monetize the QALYs gained as a result of improving GRFS outcomes. Results: Over a lifetime, the average per-patient medical cost of allo-HCT was estimated to range from $942,373 to $1,247,917. Of those total costs, 23% to 30% were incurred within the 1st 100 days and 42% to 56% within the 1st year. Of the medical costs estimated, the majority of the costs were for chronic GVHD treatment (37% to 53%) followed by the allo-HCT procedure (15% to 19%). Expected lifetime QALYs of an allo-HCT patient were estimated to be 4.7. In the scenario analyses, the unmet need that could be addressed by improving GRFS outcomes (via the medical cost offsets and QALYs gained) was associated with a net monetary value that ranged from $695,709 to $911,062. Of the estimated net monetary value, 85% to 88% was driven by direct medical cost savings while the remaining 12% to 15% was due to value of the gain in expected QALYs. Conclusions: The current per-patient cost of allo-HCT carries a substantial medical cost burden often exceeding $1M over the lifetime of the patient. Innovative research efforts focused on disease control and reduction or elimination of late complications altogether, particularly cGVHD, may provide the greatest value in improved patient outcomes and reduced healthcare spending. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal