How well do paramedics predict admission to the hospital? A prospective study

Levine, Saul V.; Colwell, Christopher B.; Pons, Peter T.; Gravitz, Craig; Haukoos, Jason S.; McVaney, Kevin

doi:10.1016/j.jemermed.2005.08.007

Cited by 77 publications

(74 citation statements)

References 16 publications

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“…17 Three studies have evaluated the ability of U.S. paramedics to predict the need for hospital admission or intensive care unit admission. Paramedics' predictions of the need for hospital admission have a negative predictive value ranging from 0.83 to 0.90, [47][48][49] and predictions about the need for intensive care unit admission have a negative predictive value ranging from 0.96 to 0.98. 47,48 Hospitalization and intensive care unit admission, however, can be misleading reference standards.…”

Section: Ems Determinations Of Medical Necessity For Emergency Departmentioning

confidence: 99%

“…Paramedics' predictions of the need for hospital admission have a negative predictive value ranging from 0.83 to 0.90, [47][48][49] and predictions about the need for intensive care unit admission have a negative predictive value ranging from 0.96 to 0.98. 47,48 Hospitalization and intensive care unit admission, however, can be misleading reference standards. Many patients who legitimately require ambulance transport or ED care may not require hospital admission; conversely, many patients admitted to a hospital do not arrive by ambulance or present to the ED.…”

Section: Ems Determinations Of Medical Necessity For Emergency Departmentioning

confidence: 99%

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EMS Provider Determinations of Necessity for Transport and Reimbursement for EMS Response, Medical Care, and Transport: Combined Resource Document for the National Association of EMS Physicians Position Statements

Millin

Brown²,

Schwartz

2011

Prehospital Emergency Care

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With increasing demands for emergency medical services (EMS), many EMS jurisdictions are utilizing EMS providerinitiated nontransport policies as a method to offload potentially nonemergent patients from the EMS system. EMS provider determination of medical necessity, resulting in nontransport of patients, has the potential to avert unnecessary emergency department visits. However, EMS systems that utilize these policies must have additional education for the providers, a quality improvement process, and active physician oversight. In addition, EMS provider determination of nontransport for a specific situation should be supported by evidence in the peer-reviewed literature that the practice is safe. Further, EMS systems that do not utilize these programs should not be financially penalized. Payment for EMS services should be based on the prudent layperson standard. EMS systems that do utilize nontransport policies should be appropriately reimbursed, as this represents potential cost savings to the health care system.

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Section: Ems Determinations Of Medical Necessity For Emergency Departmentioning

confidence: 99%

Section: Ems Determinations Of Medical Necessity For Emergency Departmentioning

confidence: 99%

EMS Provider Determinations of Necessity for Transport and Reimbursement for EMS Response, Medical Care, and Transport: Combined Resource Document for the National Association of EMS Physicians Position Statements

Millin

Brown²,

Schwartz

2011

Prehospital Emergency Care

View full text Add to dashboard Cite

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“…Also, a system capable of accurately establishing the probabilities of inpatient admission (hospitalization) for every ED patient right after triage can help streamline operations and establish priorities for clinical personnel, bed managers and supporting personnel. Previous research had shown that clinical triage 1 personnel could not predict the need for inpatient admission with sufficient reliability [21][22][23], however, models with a manageable number of easily-obtainable variables and a simple procedure for calculating the probabilities of admission could be used to aid in this task [24][25][26]. In both cases (ED census forecasts and prediction of probabilities of inpatient admission from the ED) predictive models with varying degrees of sophistication can be developed.…”

Section: Glossary Of Terms and Abbreviationsmentioning

confidence: 99%

“…The prediction of inpatient admission (hospitalization) from the emergency department right after triage (and before being seen by a physician) is a problem with a sizable degree of uncertainty performed by human operators, who are frequently under stress [21][22][23]. This problem was deemed to be an adequate application domain for classification algorithms based on machine learning techniques, based on expert knowledge and previous research [115][116][117].…”

Section: The Case For Decision Support Systems In Medicinementioning

confidence: 99%

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Design and evaluation of analytical tools for emergency department management based on machine learning techniques

Enaliev¹

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The Spanish National Healthcare System (NHS) is mostly publicly funded and provided. It is considered highly cost-efficient according to international studies based on World Health Organization (WHO) data. However, the contention of healthcare costs increases while maintaining adequate levels of quality of care, is still a largely unsolved problem. In recent years, Emergency Departments (EDs) of specialized care hospitals have been subjected to budget restrictions, increased visits and increased clinical complexity of these visits. These circumstances require new approaches to ED management, which could benefit from decision support tools.In this Ph.D. thesis, we propose machine learning solutions for two problems common to most EDs of specialized care hospitals: ED census forecasting and real-time prediction of probabilities of inpatient admission for all triaged patients in the ED. These solutions could be used as decision support systems. Data for the development of these solutions were The first topic of this Ph.D. thesis is the development of models for ED census forecasting (i.e. prediction of the number of patients present at the ED at a given time). One of the uses of ED census forecasting is nursing personnel allocation, based on national and international recommendations. We chose an 8-hour granularity for our forecasts since many resources (such as nursing personnel) in the ED are organized in 8-hour shifts. Our aim was to generate forecasts for two dependent variables: average ED census levels and maximum ED census levels. Maximum ED census forecasts within 8-hour shifts could be used for nursing personnel allocation, while average ED census forecasts within 8-hour shifts could be used for the other needs (such as allocation of administrative personnel).We used a generalized regression approach to time series forecasting with several machine learning algorithms: M5P, Alternating Model Trees (AMT) and Support Vector Regression (SVR). We compared these to a series of benchmarks: usual nursing staffing levels (and usual resource allocation policies), stratified average (averages stratified by the three 8-10 hour shifts of a day), linear regression and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Forecasts were generated for both dependent variables: average ED census levels and maximum ED census levels. Four forecast horizons were tested: 1 week, 2 weeks, 4 weeks and 8 weeks. Underestimation risks, overestimation risks and approximations to monetary costs of resource allocations policies were defined for both average and maximum ED census forecasts. Maximum ED census forecasts were transformed into nursing personnel levels, and underestimation and overestimation risks for maximum ED census forecasts were transformed into understaffing and overstaffing risks. A custom training and evaluation scheme was used, with increasingly larger train sets and fixed-length test sets. The same scheme but with fixed-length train sets of 1 year and fixed-length test sets was also used. The ...

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Prediction of Critical Illness During Out-of-Hospital Emergency Care

Seymour

Kahn

Cooke

et al. 2010

JAMA

141

162

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OSPITALS VARY WIDELY INquality of critical care. 1 Consequently, the outcomes of critically ill patients may be improved by concentrating care at more experienced centers. [1][2][3] By centralizing patients who are at greater risk of mortality in referral hospitals, regionalized care in critical illness may achieve improvements in outcome similar to trauma networks. 4 In 2006, the Institute of Medicine called for a regionalized, coordinated system of emergency care for high-risk patients, 5 one in which patients in most need of highintensity acute care are distributed to centers with the greatest expertise in caring for the critically ill.Current out-of-hospital triage of noninjured, critically ill patients uses dispatch criteria, 6 subjective emergency medical services (EMS) assessments, 7,8 coordination by medical command officers, 9 and patient preference. 10 In specific conditions such as coronary artery disease and stroke, out-of-hospital care providers use objective tools to triage and risk-stratify prehospital patients for early treatment and choice of destination. [11][12][13] However, these subjective and disease-specific assessments alone may not be sufficient for triage in general populations at risk of critical ill-ness. 8,[14][15][16] Future development of regionalized systems of acute care will require objective, routinely measured predictors that are associated with important clinical end points in a heterogeneous population. An objective triage tool may also identify patients for early treatment by out-of-hospital care providers.We sought to develop a tool for prediction of critical illness during out-ofhospital care in noninjured, non-cardiac arrest patients. Using a population-based cohort of EMS records linked to hospital discharge data, we hypothesized that objective,out-of-hospitalfactorscoulddiscriminate between patients who were and For editorial comment see p 797.

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How well do paramedics predict admission to the hospital? A prospective study

Cited by 77 publications

References 16 publications

EMS Provider Determinations of Necessity for Transport and Reimbursement for EMS Response, Medical Care, and Transport: Combined Resource Document for the National Association of EMS Physicians Position Statements

EMS Provider Determinations of Necessity for Transport and Reimbursement for EMS Response, Medical Care, and Transport: Combined Resource Document for the National Association of EMS Physicians Position Statements

Design and evaluation of analytical tools for emergency department management based on machine learning techniques

Prediction of Critical Illness During Out-of-Hospital Emergency Care

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