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10.1093/ajhp/zxab152 http://scihub22266oqcxt.onion/10.1093/ajhp/zxab152 33821926!8271205!33821926     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Am+J+Health+Syst+Pharm 2021 ; 78 (14): 1309-1316 Nephropedia Template TP {{tp|p=33821926|t=2021. Development and validation of a predictive model to predict and manage drug shortages |pdf=|usr=}}{{33821926}} gab.com Text Development and validation of a predictive model to predict and manage drug shortages JO: Am J Health Syst Pharm http://www.kidney.de/pget.php?&tw=1&pmid=33821926 #FOAvip PURPOSE: Pharmacy departments across the country are problem-solving the growing issue of drug shortages. We aim to change the drug shortage management strategy from a reactive process to a more proactive approach using predictive data analytics. By doing so, we can drive our decision-making to more efficiently manage drug shortages. METHODS: Internal purchasing, formulary, and drug shortage data were reviewed to identify drugs subject to a high shortage risk ("shortage drugs") or not subject to a high shortage risk ("nonshortage drugs"). Potential candidate predictors of drug shortage risk were collected from previous literature. The dataset was trained and tested using 2 methods, including k-fold cross-validation and a 70/30 partition into a training dataset and a testing dataset, respectively. RESULTS: A total of 1,517 shortage and nonshortage drugs were included. The following candidate predictors were used to build the dataset: dosage form, therapeutic class, controlled substance schedule (Schedule II or Schedules III-V), orphan drug status, generic versus branded status, and number of manufacturers. Predictors that positively predicted shortages included classification of drugs as intravenous-only, both oral and intravenous, antimicrobials, analgesics, electrolytes, anesthetics, and cardiovascular agents. Predictors that negatively predicted a shortage included classification as an oral-only agent, branded-only agent, antipsychotic, Schedule II agent, or orphan drug, as well as the total number of manufacturers. The calculated sensitivity was 0.71; the specificity, 0.93; the accuracy, 0.87; and the C statistic, 0.93. CONCLUSION: The study demonstrated the use of predictive analytics to create a drug shortage model using drug characteristics and manufacturing variables. Twit Text FOAVip Development and validation of a predictive model to predict and manage drug shortages ????Am J Health Syst Pharm http://www.kidney.de/pget.php?&tw=1&pmid=33821926 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33821926 #FOAvip English Wikipedia <ref>{{Cite pmid|33821926}}</ref> Development and validation of a predictive model to predict and manage drug shortages #MMPMID33821926 Liu I; Colmenares E; Tak C; Vest MH; Clark H; Oertel M; Pappas A Am J Health Syst Pharm 2021[Jul]; 78 (14): 1309-1316 PMID33821926show ga PURPOSE: Pharmacy departments across the country are problem-solving the growing issue of drug shortages. We aim to change the drug shortage management strategy from a reactive process to a more proactive approach using predictive data analytics. By doing so, we can drive our decision-making to more efficiently manage drug shortages. METHODS: Internal purchasing, formulary, and drug shortage data were reviewed to identify drugs subject to a high shortage risk ("shortage drugs") or not subject to a high shortage risk ("nonshortage drugs"). Potential candidate predictors of drug shortage risk were collected from previous literature. The dataset was trained and tested using 2 methods, including k-fold cross-validation and a 70/30 partition into a training dataset and a testing dataset, respectively. RESULTS: A total of 1,517 shortage and nonshortage drugs were included. The following candidate predictors were used to build the dataset: dosage form, therapeutic class, controlled substance schedule (Schedule II or Schedules III-V), orphan drug status, generic versus branded status, and number of manufacturers. Predictors that positively predicted shortages included classification of drugs as intravenous-only, both oral and intravenous, antimicrobials, analgesics, electrolytes, anesthetics, and cardiovascular agents. Predictors that negatively predicted a shortage included classification as an oral-only agent, branded-only agent, antipsychotic, Schedule II agent, or orphan drug, as well as the total number of manufacturers. The calculated sensitivity was 0.71; the specificity, 0.93; the accuracy, 0.87; and the C statistic, 0.93. CONCLUSION: The study demonstrated the use of predictive analytics to create a drug shortage model using drug characteristics and manufacturing variables. |*Drug Industry[MESH] |*Pharmacies[MESH] |Commerce[MESH] |Drugs, Generic[MESH]Development and validation of a predictive model to predict and manage(epmc).pdf DeepDyve Pubget Overpricing