Hospital food management: a multi-objective approach to reduce waste and costs.

Food waste contributes significantly to greenhouse emissions and represents a substantial portion of overall waste within hospital facilities. Furthermore, uneaten food leads to a diminished nutritional intake for patients, that typically are vulnerable and ill. Therefore, this study developed mathematical models for constructing patient meals in a 1000-bed hospital located in Florida. The objective is to minimize food waste and meal-building costs while ensuring that the prepared meals meet the required nutrients and caloric content for patients. To accomplish these objectives, four mixed-integer programming models were employed, incorporating binary and continuous variables. The first model establishes a baseline for how the system currently works. This model generates the meals without minimizing waste or cost. The second model minimizes food waste, reducing waste up to 22.53 % compared to the baseline. The third model focuses on minimizing meal-building costs and achieves a substantial reduction of 37 %. Finally, a multi-objective optimization model was employed to simultaneously reduce both food waste and cost, resulting in reductions of 19.70 % in food waste and 32.66 % in meal-building costs. The results demonstrate the effectiveness of multi-objective optimization in reducing waste and costs within large-scale food service operations.

A Multistate Decomposition Analysis of Cesarean Rate Variations, Associated Health Outcomes, and Financial Implications in the United States.

OBJECTIVES: Cesarean rates vary widely across the U.S. states; however, little is known about the causes and implications associated with these variations. The objectives of this study were to quantify the contribution of the clinical and nonclinical factors in explaining the difference in cesarean rates across states and to investigate the associated health outcome of cesarean variations. STUDY DESIGN: Using the Hospital Cost and Utilization Project State Inpatient Databases, this retrospective study included all nonfederal hospital births from Wisconsin, Florida, and New York. A nonlinear extension of the Oaxaca-Blinder method was used to decompose the contributions of differences in characteristics to cesarean variations between these states. The risk factors for cesarean delivery were identified using separate multivariable logistic regression analysis for each State. RESULTS: The difference in clinical and nonclinical factors explained a substantial (~46.57-65.45%) proportion of cesarean variations between U.S. states. The major contributors of variation were patient demographics, previous cesareans, hospital markup ratios, and social determinants of health. Cesarean delivery was significantly associated with higher postpartum readmissions and unplanned emergency department visits, greater lengths of stay, and hospital costs across all states. CONCLUSION: Although a proportion of variations in cesarean rates can be explained by the differences in risk factors, the remaining unexplained variations suggest differences in practice patterns and imply potential quality concerns. Since nonclinical factors are likely to play an important role in cesarean variation, we recommend targeted initiatives increasing access to maternal care and improving maternal health literacy. KEY POINTS: · Cesarean rates vary widely almost two folds within U.S. states.. · The difference in risk factors explained substantial (~46.57-65.45%) of the cesarean variations.. · Mother race, hospital factors, and social determinants comprised major proportion of explained variation.. · Adverse outcomes and increased expenditures were associated with cesarean than vaginal delivery.. · Significant potential cost savings for Medicaid if the unnecessary cesarean deliveries are reduced..

A strategic gaming model for health information exchange markets.

Current market conditions create incentives for some providers to exercise control over patient data in ways that unreasonably limit its availability and use. Here we develop a game theoretic model for estimating the willingness of healthcare organizations to join a health information exchange (HIE) network and demonstrate its use in HIE policy design. We formulated the model as a bi-level integer program. A quasi-Newton method is proposed to obtain a strategy Nash equilibrium. We applied our modeling and solution technique to 1,093,177 encounters for exchanging information over a 7.5-year period in 9 hospitals located within a three-county region in Florida. Under a set of assumptions, we found that a proposed federal penalty of up to $2,000,000 has a higher impact on increasing HIE adoption than current federal monetary incentives. Medium-sized hospitals were more reticent to adopt HIE than large-sized hospitals. In the presence of collusion among multiple hospitals to not adopt HIE, neither federal incentives nor proposed penalties increase hospitals' willingness to adopt. Hospitals' apathy toward HIE adoption may threaten the value of inter-connectivity even with federal incentives in place. Competition among hospitals, coupled with volume-based payment systems, creates no incentives for smaller hospitals to exchange data with competitors. Medium-sized hospitals need targeted actions (e.g., outside technological assistance, group purchasing arrangements) to mitigate market incentives to not adopt HIE. Strategic game theoretic models help to clarify HIE adoption decisions under market conditions at play in an extremely complex technology environment.

Activating clinical trials: a process improvement approach.

BACKGROUND: The administrative process associated with clinical trial activation has been criticized as costly, complex, and time-consuming. Prior research has concentrated on identifying administrative barriers and proposing various solutions to reduce activation time, and consequently associated costs. Here, we expand on previous research by incorporating social network analysis and discrete-event simulation to support process improvement decision-making. METHODS: We searched for all operational data associated with the administrative process of activating industry-sponsored clinical trials at the Office of Clinical Research of the University of South Florida in Tampa, Florida. We limited the search to those trials initiated and activated between July 2011 and June 2012. We described the process using value stream mapping, studied the interactions of the various process participants using social network analysis, and modeled potential process modifications using discrete-event simulation. RESULTS: The administrative process comprised 5 sub-processes, 30 activities, 11 decision points, 5 loops, and 8 participants. The mean activation time was 76.6 days. Rate-limiting sub-processes were those of contract and budget development. Key participants during contract and budget development were the Office of Clinical Research, sponsors, and the principal investigator. Simulation results indicate that slight increments on the number of trials, arriving to the Office of Clinical Research, would increase activation time by 11 %. Also, incrementing the efficiency of contract and budget development would reduce the activation time by 28 %. Finally, better synchronization between contract and budget development would reduce time spent on batching documentation; however, no improvements would be attained in total activation time. CONCLUSION: The presented process improvement analytic framework not only identifies administrative barriers, but also helps to devise and evaluate potential improvement scenarios. The strength of our framework lies in its system analysis approach that recognizes the stochastic duration of the activation process and the interdependence between process activities and entities.

A user needs assessment to inform health information exchange design and implementation.

BACKGROUND: Important barriers for widespread use of health information exchange (HIE) are usability and interface issues. However, most HIEs are implemented without performing a needs assessment with the end users, healthcare providers. We performed a user needs assessment for the process of obtaining clinical information from other health care organizations about a hospitalized patient and identified the types of information most valued for medical decision-making. METHODS: Quantitative and qualitative analysis were used to evaluate the process to obtain and use outside clinical information (OI) using semi-structured interviews (16 internists), direct observation (750 h), and operational data from the electronic medical records (30,461 hospitalizations) of an internal medicine department in a public, teaching hospital in Tampa, Florida. RESULTS: 13.7 % of hospitalizations generate at least one request for OI. On average, the process comprised 13 steps, 6 decisions points, and 4 different participants. Physicians estimate that the average time to receive OI is 18 h. Physicians perceived that OI received is not useful 33-66 % of the time because information received is irrelevant or not timely. Technical barriers to OI use included poor accessibility and ineffective information visualization. Common problems with the process were receiving extraneous notes and the need to re-request the information. Drivers for OI use were to trend lab or imaging abnormalities, understand medical history of critically ill or hospital-to-hospital transferred patients, and assess previous echocardiograms and bacterial cultures. About 85 % of the physicians believe HIE would have a positive effect on improving healthcare delivery. CONCLUSIONS: Although hospitalists are challenged by a complex process to obtain OI, they recognize the value of specific information for enhancing medical decision-making. HIE systems are likely to have increased utilization and effectiveness if specific patient-level clinical information is delivered at the right time to the right users.

Human error, not communication and systems, underlies surgical complications.

OBJECTIVE: This study prospectively assesses the underlying errors contributing to surgical complications over a 12-month period in a complex academic department of surgery using a validated scoring template. BACKGROUND: Studies in "high reliability organizations" suggest that systems failures are responsible for errors. Reports from the aviation industry target communication failures in the cockpit. No prior studies have developed a validated classification system and have determined the types of errors responsible for surgical complications. METHODS: A classification system of medical error during operation was created, validated, and data collected on the frequency, type, and severity of medical errors in 9,830 surgical procedures. Statistical analysis of concordance, validity, and reliability were performed. RESULTS: Reported major complications occurred in 332 patients (3.4%) with error in 78.3%: errors in surgical technique (63.5%), judgment errors (29.6%), inattention to detail (29.3%), and incomplete understanding (22.7%). Error contributed more than 50% to the complication in 75%. A total of 13.6% of cases had error but no injury, 34.4% prolongation of hospitalization, 25.1% temporary disability, 8.4% permanent disability, and 16.0% death. In 20%, the error was a "mistake" (the wrong thing), and in 58% a "slip" (the right thing incorrectly). System errors (2%) and communication errors (2%) were infrequently identified. CONCLUSIONS: After surgical technique, most surgical error was caused by human factors: judgment, inattention to detail, and incomplete understanding, and not to organizational/system errors or breaks in communication. Training efforts to minimize error and enhance patient safety must address human factor causes of error.