Observational Medical Outcomes

OMO (observational medical outcome) A parameter obtained from a patient record of which an adverse drug reaction (q.v.) is of particular, but not sole, interest. Usually OMOs are collected from many patient records and used in outcome analysis (q.v.). 

OMOP (observational medical outcomes partnership) Used genetically, any partnership, usually of pharmaceutical companies, applying pharmacosur-veillance (q.v.) or pharmacovigilance (q.v.) to patients. Sometimes elsewhere in the literature the P stands for pilot, process, or procedure. 

Choices Lessons from the Observational Medical Outcomes Partnership.133... 

Stang, RE. et al.. Advancing the science for active surveillance Rationale and design for the Observational Medical Outcomes Partnership. Ann Intern Med, 153(9) 600-606,2010. 

Ryan PB, Madigan D, Stang PE, Marc Overhage J, Racoosin JA, Hartzema AG. 2012. Empirical assessment of methods for risk identification in healthcare data Results from the experiments of the Observational Medical Outcomes Partnership. Stat. Med. 31(30) 4401-4415. 

Discusses the observational medical outcomes partnership (OMOP)... 

In 1980-81, the Army asked the National Research Council of the National Academy of Sciences to review the medical outcomes of all volunteers tested from 1955-1975. Among the belladonnoid drug subjects, observed deaths were actually found to be fewer than the number expected. The policy of selecting only the healthiest candidates to serye as volunteers no doubt accounts, at least in part, for this favorable finding. It is ironic... 

Once a medical need and market niche are identified, and a particular therapeutic area chosen, the biological research begins. It is during this first stage of drug discovery that anecdotal clinical observations, empirical outcomes and data from folk medicine are often employed, if only as direction-finding tools. 

Thus, a program of ongoing research may well be needed which calls upon the skills of the phar-macoepidemiologist, observational studies of drug utilization patterns, often tied to medical outcomes. 

The sampling distribution determined in the previous section is an example of a deductive use of probability. Given that the probability of an occurrence of a one or two is known, we were able to deduce the probability of the outcomes that could arise if the die was tossed 10 times. In medical research, however we do not know what the true probability (response probability) is. Ours is the reverse problem, we observe a response rate, for example, 23 out of 80 patients respond positively to a given treatment, and want to infer what the true population response rate is. The requirement is to be able to make inductive probability statements. 

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