Nearly 20% of patients are readmitted to hospitals within a specific time period after hospital discharge. High readmission rates place an unnecessary burden on the healthcare system, and new ...initiatives to reduce preventable hospital readmissions have been established. The United States Hospital Readmission Reduction Program (HRRP) is an example of a health policy reform that links insurance payments to quality of care. Critics of HRRP believe that its punitive mechanism design provides less money to struggling hospitals and, in some cases, fails to provide proper incentives and resources for quality care improvements. An asymmetric penalty-incentive model for hospital readmission reductions was developed and studied for an insurer-led reimbursement system. We formulate a game-theoretic setting involving an insurer and a hospital. We derive the insurer’s optimal policy design and the hospital’s best response in an insurer-led Stackelberg setting with rational agents. The model was analyzed for centralized and decentralized solutions and compared to the do-nothing solution. Most notably, we found that a positive incentive level is necessary for a win-win region to exist. An example from public hospital data for acute myocardial infarction showed that transitioning from the current 3% penalty-only policy to the optimal 5.47% incentive-only policy would result in only a 0.17% increase in insurer costs while inspiring hospitals to maximize level of care and increase hospital profits by 39.7%.
•Model and optimize asymmetric hospital readmission reduction mechanism designs.•Formulate a game-theoretic setting between an insurer and a hospital.•Identify win-win regions in which both agents are better off compared to do-nothing.•Penalty-only policies are optimal for insurers but have the worst hospital outcome.•Positive incentive levels are necessary for win-win regions to exist.
Poly(3-hydroxybutyrate) (PHB), a bio-produced and biodegradable polymer, has great potential as a replacement for petroleum-based polymers in many applications. However, strategies for the extraction ...and processing of PHB still require improvement. Switchable solvents, which can be toggled between hydrophobic and hydrophilic forms by the addition or removal of carbon dioxide in the presence of water, are easily recyclable and may improve PHB processing methods. Here, we have shown the ability to dissolve PHB in two switchable solvents (N,N-dimethylbenzylamine and N,N-dimethylcyclohexylamine), precipitate PHB by the addition of water and carbon dioxide, and recycle the solvent for subsequent dissolution and precipitation cycles. We have also demonstrated the ability for N,N-dimethylbenzylamine to form gels with PHB which maintain their water/solvent content as the solvent is switched to a hydrophilic form. These results demonstrate the usefulness of switchable solvents as a recyclable platform for PHB processing and their ability to create unique materials.
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•Switchable hydrophilicity solvents were shown to dissolve and precipitate PHB•Recovered solvent could be recycled for subsequent dissolution and precipitation•The presence of water decreased PHB molecular weight but improved dissolution•PHB-solvent gels could still have solvent switched to hydrophilic form•Switched gels were soft and could be dried into molded shapes
Online healthcare forums (OHFs) have become increasingly popular for patients to share their health-related experiences. The healthcare-related texts posted in OHFs could help doctors and patients ...better understand specific diseases and the situations of other patients. To extract the meaning of a post, a commonly used way is to classify the sentences into several predefined categories of different semantics. However, the unstructured form of online posts brings challenges to existing classification algorithms. In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications. To tackle the challenges above, we propose an effective and interpretable OHF post classification framework. Specifically, we classify sentences into three classes: medication, symptom, and background. Each sentence is projected into an interpretable feature space consisting of labeled sequential patterns, UMLS semantic types, and other heuristic features. A forest-based model is developed for categorizing OHF posts. An interpretation method is also developed, where the decision rules can be explicitly extracted to gain an insight of useful information in texts. Experimental results on real-world OHF data demonstrate the effectiveness of our proposed computational framework.
We assessed how systems science methodologies might be used to bridge resource gaps at local health departments (LHDs) so that they might better implement evidence-based decision-making (EBDM) to ...address population health challenges.
We used the New York Academy of Medicine Cardiovascular Health Simulation Model to evaluate the results of a hypothetical program that would reduce the proportion of people smoking, eating fewer than 5 fruits and vegetables per day, being physically active less than 150 minutes per week, and who had a body mass index (BMI) of 25 kg/m(2) or greater. We used survey data from the Behavioral Risk Factor Surveillance System to evaluate health outcomes and validate simulation results.
Smoking rates and the proportion of the population with a BMI of 25 kg/m(2) or greater would have decreased significantly with implementation of the hypothetical program (P < .001). Two areas would have experienced a statistically significant reduction in the local population with diabetes between 2007 and 2027 (P < .05).
The use of systems science methodologies might be a novel and efficient way to systematically address a number of EBDM adoption barriers at LHDs.
Importance
Blood pressure monitoring is critical to the timely diagnosis and treatment of hypertension. At-home self-monitoring techniques are highly effective in managing high blood pressure; ...however, evidence regarding the cost-effectiveness of at-home self-monitoring compared with traditional monitoring in clinical settings remains unclear.
Objective
To identify and synthesize published research examining the cost-effectiveness of at-home blood pressure self-monitoring relative to monitoring in a clinical setting among patients with hypertension.
Evidence Review
A systematic literature search of 5 databases (PubMed, MEDLINE, Embase, EconLit, and CINAHL) followed by a backward citation search was conducted in September 2022. Full-text, peer-reviewed articles in English including patients with high blood pressure (systolic blood pressure ≥130 mm Hg and diastolic blood pressure ≥80 mm Hg) at baseline were included. Data from studies comparing at-home self-monitoring with clinical-setting monitoring alternatives were extracted, and the outcomes of interest included incremental cost-effectiveness and cost-utility ratios. Non–peer-reviewed studies or studies with pregnant women and children were excluded. To ensure accuracy and reliability, 2 authors independently evaluated all articles for eligibility and extracted relevant data from the selected articles.
Findings
Of 1607 articles identified from 5 databases, 16 studies met the inclusion criteria. Most studies were conducted in the US (6 40%) and in the UK (6 40%), and almost all studies (14 90%) used a health care insurance system perspective to determine costs. Nearly half the studies used quality-adjusted life-years gained and cost per 1–mm Hg reduction in blood pressure as outcomes. Overall, at-home blood pressure monitoring (HBPM) was found to be more cost-effective than monitoring in a clinical setting, particularly over a minimum 10-year time horizon. Among studies comparing HBPM alone vs 24-hour ambulatory blood pressure monitoring (ABPM) or HBPM combined with additional support or team-based care, the latter were found to be more cost-effective.
Conclusions and Relevance
In this systematic review, at-home blood pressure self-monitoring, particularly using automatic 24-hour continuous blood pressure measurements or combined with additional support or team-based care, demonstrated the potential to be cost-effective long-term compared with care in the physical clinical setting and could thus be prioritized for patients with hypertension from a cost-effectiveness standpoint.
Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences.
This study aimed to develop a prediction model for ...hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods.
Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods).
This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies.
Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.
Diabetes mellitus in adults is a global health burden affecting 382 million people and costing over $612 billion worldwide. Remote patient monitoring is often considered to be a technological ...solution to the chal-lenges in healthcare delivery, yet many studies have shown mixed results or no effect on patient outcomes. A narrative review of literature was conducted to contribute to the field of technology-driven home healthcare delivery by analyzing the systems in context with the monitoring and intervention technologies. This review analyzed papers with home telemonitoring and intervention systems for adults with type 1 or type 2 diabetes. Technologies used were differentiated into four categories: telephones, mobile devices, computers, and other Internet-connected devices. Our findings suggest no clear association between the type of technology used and the outcomes of the participants. Frequency of monitoring and intervention were distinguishable by diabetic outcome metrics.
Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, nonattendance can affect continuity of care and result in higher emergency department (ED) and ...hospital use. Nonattendance also impacts appointment scheduling, patient access, and clinic work load. While no show has received significant attention, little research has addressed the prevalence and impact of appointment cancellation. Data on 46,710 appointments for 7586 adult diabetic patients was used to conduct a prospective cohort study examining primary care appointment behavior. The independent variable was the status of the INDEX appointment, which was attended, cancelled, or no showed. Dependent variables included the dates of (1) the last attended appointment, (2) scheduling the NEXT appointment, (3) the next attended follow-up appointment, and (4) ED visits and hospitalizations within six months of the INDEX. Cancellation was more prevalent than no show (17.7% vs 12.2%). Of those who cancelled and scheduled a next appointment, 28.8% experienced over 30 days delay between the INDEX and NEXT appointment dates, and 59.9% delayed rescheduling until on or after the cancelled appointment date. Delay in rescheduling was associated with an 18.6% increase in days between attended appointments and a 26.0% increase in ED visits. For diabetic patients, cancellation with late rescheduling is a prevalent and unhealthy behavior. Although more work is necessary to address the health, intervention, and cost issues, this work suggests that cancellation, like no show, may be problematic for many clinics and patients.
In this paper a stochastic overbooking model is formulated and an appointment scheduling policy is developed for outpatient clinics. The schedule is constructed for a single service period ...partitioned into time slots of equal length. A clinic scheduler assigns patients to slots through a sequential patient call-in process where the scheduler must provide each calling patient with an appointment time before the patient's call terminates. Once an appointment is added to the schedule, it cannot be changed. Each calling patient has a no-show probability, and overbooking is used to compensate for patient no-shows. The scheduling objective captures patient waiting time, staff overtime and patient revenue. Conditions under which the objective evolution is unimodal are derived and the behavior of the scheduling policy is investigated under a variety of conditions. Practical observations on the performance of the policy are presented.
As noncontact health interventions have become critical during the Covid-19 pandemic, our study aimed to systematically review the published literature for barriers and facilitators influencing the ...adoption and use of remote health intervention and technology, as perceived by adult patients with diabetes or cardiovascular diseases (CVD) belonging to groups that are socially/economically marginalized and/or medically under-resourced. We searched Medline, Embase, CINAHL, and PsychINFO for peer-reviewed articles published from 2010 to 2018. We employed content analysis to analyze qualitative patient feedback from the included studies. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A total of 42 studies met the inclusion criteria. The design of the remote health technology used was the most frequently mentioned facilitator and barrier to remote health technology adoption and use. Our results should draw the attention of technology developers to the usability and feasibility of remote technology among populations that are socially/economically marginalized and/or medically under-resourced.
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