On day admissions in neurosurgery: a comparative cohort study.

Day of admission neurosurgery is a viable method to reduce health care associated costs, complications and length of stay. Within the national health service in England the picture is very mixed with some neurosurgery centres universally admitting patients the day before and others admitting on the day of surgery.We altered our admissions policy during a 4 month time period from 'day-before' surgery to 'day-of' surgery for elective neurosurgery. A number of patients still continued to be admitted the day before surgery due to consultant choice. We conducted a comparative cohort study of these two patient groups to see if there were any differences in surgical cancellation rates, the reasons for these cancellations and the implied cost savings.In total 199 patients underwent neurosurgery during this time period, 87 patients were admitted on the 'day-of' and 112 patients on the 'day-before' surgery. The overall cancellation rate was 18%. The cancellation rate in patients admitted on the 'day-of' surgery was 12.6% (11/87). The rate of cancellation in patients admitted the 'day-before' surgery was 22.3% (25/112). This difference was not significant (p = 0.1). Day of surgery admission resulted in a cost saving of almost £30,000 in this group of patients over a 4 month period. If extrapolated for all patients over the course of a year it would result in cost savings in the region of £150,000.In summary, admitting elective neurosurgery patients on the day of surgery does not affect cancellation rates, prevents unnecessary overnight hospital admission and results in significant cost saving.

Organization of intensive cardiac care units in Europe: Results of a multinational survey.

BACKGROUND: The present survey aims to describe the intensive cardiac care unit organization and admission policies in Europe. METHODS: A total of 228 hospitals (61% academic) from 27 countries participated in this survey. In addition to the organizational aspects of the intensive cardiac care units, including classification of the intensive cardiac care unit levels, data on the admission diagnoses were gathered from consecutive patients who were admitted during a two-day period. Admission policies were evaluated by comparing illness severity with the intensive cardiac care unit level. Gross national income was used to differentiate high-income countries (n=13) from middle-income countries (n=14). RESULTS: A total of 98% of the hospitals had an intensive cardiac care unit: 70% had a level 1 intensive cardiac care unit, 76% had a level 2 intensive cardiac care unit, 51% had a level 3 intensive cardiac care unit, and 60% of the hospitals had more than one intensive cardiac care unit level. High-income countries tended to have more level 3 intensive cardiac care units than middle-income countries (55% versus 41%, p=0.07). A total of 5159 admissions were scored on illness severity: 63% were low severity, 24% were intermediate severity, and 12% were high severity. Patients with low illness severity were predominantly admitted to level 1 intensive cardiac care units, whereas patients with high illness severity were predominantly admitted to level 2 and 3 intensive cardiac care units. A policy mismatch was observed in 12% of the patients; some patients with high illness severity were admitted to level 1 intensive cardiac care units, which occurred more often in middle-income countries, whereas some patients with low illness severity were admitted to level 3 intensive cardiac care units, which occurred more frequently in high-income countries. CONCLUSION: More than one-third of the admitted patients were considered intermediate or high risk. Although patients with higher illness severity were mostly admitted to high-level intensive cardiac care units, an admission policy mismatch was observed in 12% of the patients; this mismatch was partly related to insufficient logistic intensive cardiac care unit capacity.

Data-driven optimization methodology for admission control in critical care units.

The decision of whether to admit a patient to a critical care unit is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient health risk metrics can be incorporated while considering the congestion that will occur. The hospital is modeled as a complex loss queueing network with a stochastic model of how long risk-stratified patients spend time in particular units and how they transition between units. A Mixed Integer Programming model approximates an optimal admission control policy for the network of units. While enforcing low levels of patient blocking, we optimize a monotonic dual-threshold admission policy. A hospital network including Intermediate Care Units (IMCs) and Intensive Care Units (ICUs) was considered for validation. The optimized model indicated a reduction in the risk levels required for admission, and weekly average admissions to ICUs and IMCs increased by 37% and 12%, respectively, with minimal blocking. Our methodology captures utilization and accessibility in a network model of care pathways while supporting the personalized allocation of scarce care resources to the neediest patients. The interesting benefits of admission thresholds that vary by day of week are studied.

Three Policies in The Baccalaureate Degree Program and Outcome-Based Education / 医学教育

Educational reforms are required to achieve the "learning outcomes" expected in bachelor degree education programs. In executing reform, three policies of "diploma policy" , "curriculum policy" , and "admission policy" are clearly stated for each institution in order to facilitate integrated operation and practice. For the sake of quality assurance, outcome-based education (OBE), which emphasizes "learning outcomes" , has been introduced to medical education but it has not been adopted by all learning institutions. With the implementation of field-specific evaluations for medical education comes a need for all medical departments and medical colleges to introduce OBE as soon as possible. For this reason, an examination was conducted to find out how to formulate and operate the three policies under OBE.

Ebola: Impact of hospital's admission policy in an overwhelmed scenario.

Infectious disease outbreaks sometimes overwhelm healthcare facilities. A recent case occurred in West Africa in 2014 when an Ebola virus outbreak overwhelmed facilities in Sierra Leone, Guinea and Liberia. In such scenarios, how many patients can hospitals admit to minimize disease burden? This study considers what type of hospital admission policy during a hypothetical Ebola outbreak can better serve the community, if overcrowding degrades the hospital setting. Our result shows that which policy minimizes loss to the community depends on the initial estimation of the control reproduction number, R0. When the outbreak grows extremely fast (R0 ≫ 1) it is better (in terms of total disease burden) to stop admitting patients after reaching the carrying capacity because overcrowding in the hospital makes the hospital setting ineffective at containing infection, but when the outbreak grows only a little faster than the system's ability to contain it (R0 ≳ 1), it is better to admit patients beyond the carrying capacity because limited overcrowding still reduces infection more in the community. However, when R0 is no more than a little greater than 1 (for our parameter values, 1.012), both policies result the same because the number of patients never exceeds the maximum capacity.

Selective admission policy of medical undergraduates in western China: applicants' real attitudes to the choice of a rural medical career.

INTRODUCTION: Since 2010, the Chinese government has been introducing selective admission policy to recruit rural students for 5-year western medicine and traditional Chinese medicine undergraduate education in order to improve rural townships' medical services system in western China. This study aimed to analyse the selective admission policy in western China from the perspective of medical students' attitudes towards rural career choice. METHODS: A cross-sectional survey was conducted and an anonymous questionnaire was used to investigate a sample of medical undergraduates chosen under the selective admission policy. RESULTS: The results indicate that medical undergraduates' enthusiasm to work in rural areas was very limited in Gansu province, western China. Extrinsic motivation played a more important role in rural career choice than intrinsic motivation. The students' attitudes were affected by socioeconomic and cultural conditions, which determined their personal and professional environment. Course major and family economic conditions were associated with their self-decisions. CONCLUSION: Further educational intervention should emphasise the students' humanistic inner qualities and recognition of professional value. Further policy adjustment should considered, for example improving social policy-based regional character and national development strategies.

Strategic level proton therapy patient admission planning: a Markov decision process modeling approach.

A relatively new consideration in proton therapy planning is the requirement that the mix of patients treated from different categories satisfy desired mix percentages. Deviations from these percentages and their impacts on operational capabilities are of particular interest to healthcare planners. In this study, we investigate intelligent ways of admitting patients to a proton therapy facility that maximize the total expected number of treatment sessions (fractions) delivered to patients in a planning period with stochastic patient arrivals and penalize the deviation from the patient mix restrictions. We propose a Markov Decision Process (MDP) model that provides very useful insights in determining the best patient admission policies in the case of an unexpected opening in the facility (i.e., no-shows, appointment cancellations, etc.). In order to overcome the curse of dimensionality for larger and more realistic instances, we propose an aggregate MDP model that is able to approximate optimal patient admission policies using the worded weight aggregation technique. Our models are applicable to healthcare treatment facilities throughout the United States, but are motivated by collaboration with the University of Florida Proton Therapy Institute (UFPTI).

Scheduling rules to achieve lead-time targets in outpatient appointment systems.

This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment lead-time targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the request is not rejected, the rules prescribe how to assign the patient to an available slot. The intent of the scheduling rules is to maximize the utilization of the planned resource (i.e., the physician staff), or equivalently to maximize the number of patients that are admitted, while maintaining the service targets on the median, the 95th percentile, and the maximum appointment lead-times. We test the proposed scheduling rules with numerical experiments using real data from the chosen clinic of Tan Tock Seng hospital in Singapore. The results show the efficiency and the efficacy of the scheduling rules, in terms of the service-target satisfaction and the resource utilization. From the sensitivity analysis, we find that the performance of the proposed scheduling rules is fairly robust to the specification of the established lead-time targets.