Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments

A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

How do queuing concepts and tools help to efficiently manage hospitals when the patients are impatient? A demonstration

Background: Due to severe pain, patients are impatient in several wings sporadically and more frequently in emergency wing of the hospitals. To efficiently administer in such environment and the hospital management seeks helpful strategies. The queuing concepts and related methodologies can help as this article has demonstrated by an analysis and interpretation of real data from a hospital in Malta. Methods: The queuing concepts are probabilistic and statistical ideas based approach. They require configuration of the rate and pattern of arriving patients, the rate and pattern of the service, the number of channels serving, the capacity of the waiting room, and the criterion for selecting patients for service etc. New ideas are presented in this article to manage in various scenarios of real life emergency operations. The pertinent queuing concepts and tools are made easier for the readers to comprehend and practice in their own situations in which they notice that the patients are impatient in their waiting.Results:Using the new ideas and formulas of this article, the data in the emergency wing of a hospital in Malta (a largest island of an archipelago situated in the center of the Mediterranean with a total population of a million) are analyzed and interpreted. The results clearly explain why there were a prolonged waiting times at the emergency department creating public dissatisfaction and patients were leaving without waiting to be seen. The total time spent by non-urgent patients with nurse and casualty officer is more in the second shift and lesser and lesser in the third and fourth shifts. The interactive time with a nurse by patient is statistically same in all three types: life-threatening, non-life threatening but urgent, and non-urgent. Very strikingly, the patients in all three groups wait longer to be seen by the nurse in shift three and lesser time in shifts two or four.Conclusion: In 21st century with flourishing globalized medical tourism, a standardized approach to minimize efficiently the waiting time in emergency and other wings of the hospitals in developing as much as in developed nations is a necessity as this auricle has pointed out. The impediments and the remedies for an efficient standardization are overdue.

Modelling the Perinatal Network System

The topic is that hospital capacity for patient beds runs short. We wish to predict when this will occur. An inter-disciplinary approach to this problem is taken incorporating a Management Science / Operational Research perspective. The subject is the Perinatal Network System, which is described, analysed and modelled. An illustrative Case Study is taken of an English local neonatal unit, where new-born babies are cared for. The focus is High dependency cots. Recommendations produced are subject to human factors and implementation difficulties. In this work, Systems Thinking facilitates an understanding of relationships; Enterprise Architecture helps embed the context and address complexity; while Clinical Medicine underpins decision-making for individual patients. Research outputs include the Conceptual Research Framework, a Quality Metric, a Cot Predictor Tool and a Markovian model Design, which can be adapted in the future. Furthermore there is the milieu or connective ‘glue’, to provide unity. The methodology or Enterprise Modelling helps address the issue by facilitating understanding of both overview and detail

Machine learning in healthcare : an investigation into model stability

Current machine learning algorithms, when directly applied to medical data, often fail to provide a good understanding of prognosis. This study provides three pathways to make predictive models stable and usable for healthcare. When tested on heart failure and diabetes patients from a local hospital, this study demonstrated 20% improvement over existing methods.<br /