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Risk prediction of emergency department revisit 30 days post discharge: a prospective study.

Hao S, Jin B, Shin AY, Zhao Y, Zhu C, Li Z, Hu Z, Fu C, Ji J, Wang Y, Zhao Y, Dai D, Culver DS, Alfreds ST, Rogow T, Stearns F, Sylvester KG, Widen E, Ling XB - PLoS ONE (2014)

Bottom Line: Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization.Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score.Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

View Article: PubMed Central - PubMed

Affiliation: HBI Solutions Inc., Palo Alto, California, United States of America; Department of Surgery, Stanford University, Stanford, California, United States of America.

ABSTRACT

Background: Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization.

Methods and findings: A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns.

Conclusions: Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

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Observed rates of future 30-day ED returns versus risk scores in prospective tests.
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pone-0112944-g005: Observed rates of future 30-day ED returns versus risk scores in prospective tests.

Mentions: Our ED revisit algorithm produced a risk score (from 0 to 100) for each patient at ED discharge to assess the risk of ED revisit. The trending of PPVs and sensitivities as a function of risk scores were similar in both retrospective with prospective analyses, indicating the robustness of the model (Figure 5, Table S3). The PPV values increased monotonically as the risk scores went high. When the risk score was more than 60, the model identified more than 60% of the ED 30 day revisits in prospective tests. With a risk score higher than 90, 93.5% of prospective revisits were identified correctly. At risk scores between 30 and 40 in prospective analysis, the algorithm found a fairly impressive percentage (24.4%) of all ED revisits. Sensitivities decreased with the risk increase, up to 3.0% with scores higher than 70. The receiver operating characteristic curve analyses showed that there was a 71.0% (retrospective) or 70.4% (prospective) probability that a randomly selected ED discharged patient with a 30-day post discharge ED revisit will receive a higher risk score than a randomly selected patient who will not have a future 30-day ED revisit.


Risk prediction of emergency department revisit 30 days post discharge: a prospective study.

Hao S, Jin B, Shin AY, Zhao Y, Zhu C, Li Z, Hu Z, Fu C, Ji J, Wang Y, Zhao Y, Dai D, Culver DS, Alfreds ST, Rogow T, Stearns F, Sylvester KG, Widen E, Ling XB - PLoS ONE (2014)

Observed rates of future 30-day ED returns versus risk scores in prospective tests.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231082&req=5

pone-0112944-g005: Observed rates of future 30-day ED returns versus risk scores in prospective tests.
Mentions: Our ED revisit algorithm produced a risk score (from 0 to 100) for each patient at ED discharge to assess the risk of ED revisit. The trending of PPVs and sensitivities as a function of risk scores were similar in both retrospective with prospective analyses, indicating the robustness of the model (Figure 5, Table S3). The PPV values increased monotonically as the risk scores went high. When the risk score was more than 60, the model identified more than 60% of the ED 30 day revisits in prospective tests. With a risk score higher than 90, 93.5% of prospective revisits were identified correctly. At risk scores between 30 and 40 in prospective analysis, the algorithm found a fairly impressive percentage (24.4%) of all ED revisits. Sensitivities decreased with the risk increase, up to 3.0% with scores higher than 70. The receiver operating characteristic curve analyses showed that there was a 71.0% (retrospective) or 70.4% (prospective) probability that a randomly selected ED discharged patient with a 30-day post discharge ED revisit will receive a higher risk score than a randomly selected patient who will not have a future 30-day ED revisit.

Bottom Line: Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization.Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score.Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

View Article: PubMed Central - PubMed

Affiliation: HBI Solutions Inc., Palo Alto, California, United States of America; Department of Surgery, Stanford University, Stanford, California, United States of America.

ABSTRACT

Background: Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization.

Methods and findings: A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns.

Conclusions: Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

Show MeSH