Features: APhA Drug Treatment Protocols: Comprehensive Weight Management in Adults

2001 ◽  
Vol 41 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Daniel H. Albrant
Keyword(s):  
Drug Treatment ◽  
Weight Management ◽  
Treatment Protocols

Artificial Intelligence in Drug Treatment

2020 ◽  
Vol 60 (1) ◽  
pp. 353-369 ◽  
Author(s):  
Eden L. Romm ◽  
Igor F. Tsigelny
Keyword(s):  
Artificial Intelligence ◽  
Drug Treatment ◽  
Drug Interactions ◽  
Drug Target ◽  
Process Selection ◽  
Treatment Protocols ◽  
Optimal Drug ◽  
Chemical Descriptors ◽  
Pharmacodynamic Data ◽  
Selection Of

The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.

Pharmacy-Enforced Outpatient Drug Treatment Protocols: A Case Study of Medi-Cal Restrictions for Cefaclor

1993 ◽  
Vol 27 (2) ◽  
pp. 155-161 ◽  
Author(s):  
Jeffrey S. Mccombs ◽  
Michael B. Nichol
Keyword(s):  
High Risk ◽  
Drug Treatment ◽  
Outcome Measures ◽  
Treatment Protocol ◽  
Healthcare Expenditures ◽  
Treatment Protocols ◽  
High Risk Patients ◽  
Risk Patients ◽  
Outpatient Drug

Objective To evaluate whether a pharmacy-enforced treatment protocol successfully limited the use of a high-cost medication to high-risk patients. Design A case study cost-effectiveness analysis was conducted to evaluate a treatment protocol for cefaclor. Episodes of care were defined, healthcare expenditures for all services were aggregated, and demographic data were retrieved from a five percent random sample of California Medicaid (Medi-Cal) recipients. Data were available for episodes occurring before cefaclor was made available under Medi-Cal. Setting Medi-Cal added cefaclor to its formulary, limiting its use to patients over 50 years of age with lower respiratory tract infections (LRTIs). The unit of analysis was an episode of outpatient antibiotic treatment. Patients Confirmed LRTI episodes and unconfirmed LRTI cefaclor episodes were analyzed, including multiple episodes of treatment for individual patients. A total of 7855 non-cefaclor LRTI episodes and 2556 cefaclor episodes were analyzed. Main outcome Measures The primary outcome measures were healthcare expenditures three months after the initiation of antibiotic therapy, differentiated by type of service. Results Physicians directed cefaclor toward higher-risk patients over age 50 years, even in unconfirmed LRTI episodes. Cefaclor use was estimated to reduce posttreatment costs by $388 per patient (p<0.001), primarily because of reduced hospital expenditures of $366 (p<0.001). Conclusions Pharmacy-enforced outpatient drug treatment protocols may be a viable alternative to restrictive formularies and prior authorization. In the case of cefaclor, the Medi-Cal treatment protocol appeared to allow high-risk patients better access to a high-cost medication while reducing total posttreatment costs.

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