Using claims data to generate clinical flags predicting short-term risk of continued psychiatric hospitalizations.
Using claims data to generate clinical flags predicting short-term risk of continued psychiatric hospitalizations.
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使用索赔数据生成临床标志，预测持续精神科住院治疗的短期风险。
DOI：
10.1176/appi.ps.201300306
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Essock,SusanM
中科院分区：
文献类型：
--
作者：
Stein,BradleyD;Pangilinan,Maria;Sorbero,MarkJ;Marcus,SueM;Donahue,SheilaA;Xu,Yan;Smith,ThomasE;Essock,SusanM
ObjectiveAs health information technology advances, efforts to use administrative data to inform real-time treatment planning for individuals are increasing, despite few empirical studies demonstrating that such administrative data predict subsequent clinical events. Medicaid claims for individuals with frequent psychiatric hospitalizations were examined to test how well patterns of service use predict subsequent high short-term risk of continued psychiatric hospitalizations.MethodsMedicaid claims files from New York and Pennsylvania were used to identify Medicaid recipients ages 18–64 with two or more inpatient psychiatric admissions during a target year ending March 31, 2009. Definitions from a quality-improvement initiative were used to identify patterns of inpatient and outpatient service use and prescription fills suggestive of clinical concerns. Generalized estimating equations and Markov models were applied to examine claims through March 2011, to see what patterns of service use were sufficiently predictive of additional hospitalizations to be clinically useful.ResultsA total of 11,801 individuals in New York and 1,859 in Pennsylvania identified met the cohort definition. In both Pennsylvania and New York, multiple recent hospitalizations, but not failure to use outpatient services or failure to fill medication prescriptions, were significant predictors of high risk of continued frequent hospitalizations, with odds ratios greater than 4.0.ConclusionsAdministrative data can be used to identify individuals at high risk of continued frequent hospitalizations. Payers and system administrators could use such information to authorize special services (such as mobile outreach) for such individuals to promote service engagement and prevent rapid rehospitalizations.
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影响因子：
--
作者：
Olfson, Mark;Marcus, Steven C.;Bridge, Jeffrey A.
通讯作者：
Bridge, Jeffrey A.
影响因子：
12.7
作者：
Marcus, Steven C.;Bridge, Jeffrey A.;Olfson, Mark
通讯作者：
Olfson, Mark
一项干预措施，以改善住院频繁入院的高危患者的护理和成本：一项试点研究。
影响因子：
2.8
作者：
Raven MC;Doran KM;Kostrowski S;Gillespie CC;Elbel BD
通讯作者：
Elbel BD
影响因子：
17.7
作者：
Gilmer, TP;Dolder, CR;Jeste, DV
通讯作者：
Jeste, DV
影响因子：
3
作者：
Riley, Gerald F.
通讯作者：
Riley, Gerald F.