1,801 research outputs found
Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS
We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making
Simulation and Modeling for Improving Access to Care for Underserved Populations
Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs)
in Indiana, constructed effective outpatient appointment scheduling systems by
determining care needs of CHC patients, designing an infrastructure for meaningful use of
patient health records and clinic operational data, and developing prediction and simulation
models for improving access to care for underserved populations. The aims of this study
are 1) redesigning appointment scheduling templates based on patient characteristics,
diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive
modeling to improve understanding the complexity of appointment adherence in
underserved populations; and 3) developing simulation models with complex data to guide
operational decision-making in community health centers. This research addresses its aims
by applying a multi-method approach from different disciplines, such as statistics,
industrial engineering, computer science, health informatics, and social sciences. First, a
novel method was developed to use Electronic Health Record (EHR) data for better
understanding appointment needs of the target populations based on their characteristics
and reasons for seeking health, which helped simplify, improve, and redesign current
appointment type and duration models. Second, comprehensive and informative predictive
models were developed to better understand appointment non-adherence in community
health centers. Logistic Regression, NaĆÆve Bayes Classifier, and Artificial Neural Network
found factors contributing to patient no-show. Predictors of appointment non-adherence
might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems
in CHCs, and necessary steps to extract information for simulation modeling of scheduling
systems in CHCs are described. Agent-Based Models were built in AnyLogic to test
different scenarios of scheduling methods, and to identify how these scenarios could impact
clinic access performance. This research potentially improves well-being of and care
quality and timeliness for uninsured, underinsured, and underserved patients, and it helps
clinics predict appointment no-shows and ensures scheduling systems are capable of
properly meeting the populationsā care needs.2021-12-2
Designing Effective Physician Incentives: Assessing the Relationship between Patient Satisfaction and Clinical Quality in an Ambulatory Environment
As the United State healthcare system continues to evolve from a reimbursement system based on volume to one based on value, understanding the relationship between physician quality metrics such as patient satisfaction and clinical quality metrics is extremely important. In order to improve value by effectuating behavior change, physician financial incentives must be designed based on desired outcomes. Understanding the relationship between performance indicators and aligning incentives is integral to successfully incentivizing physician behavior change. This study assessed the relationship between patient satisfaction and clinical quality in an ambulatory setting and determined that they are separate domains, but certain types of clinical quality are identifiable by patients and thus impact satisfaction
Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment
Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates
Patient No-Show Prediction: A Systematic Literature Review
Nowadays, across the most important problems faced by health centers are those caused by
the existence of patients who do not attend their appointments. Among others, these patients
cause loss of revenue to the health centers and increase the patientsā waiting list. In order to
tackle these problems, several scheduling systems have been developed. Many of them require
predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed.
Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research
Sentara Healthcare: A Case Study Series on Disruptive Innovation Within Integrated Health Systems
Examines how integration and ties with health plans, physicians, and hospitals helped protect against revenue volatility and enabled experimentation; factors that facilitate integration; innovative practices; lessons learned; and policy implications
Adviser\u27s Guide to Health Care, Volume 2: Consulting Services
https://egrove.olemiss.edu/aicpa_guides/2721/thumbnail.jp
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