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High quality data collection is imperative to the quality and ac= curacy of analytics results, irrespective of the terminology used. Whether = the focus is decision support, business intelligence, research or a mixture= of all three - data quality is critical. High quality information is not t= he consequence of collecting as much data as possible. Instead, it is the p= roduct of intentionality and process design.
The factors that may impact the quality of patient data include:
Clinical user interfaces should be designed to make it as easy as possib= le to find the most appropriate code, and as difficult as possible to enter= the wrong code. There are a variety of ways to improve the ease and effect= iveness of data entry using SNOMED CT =E2=80=93 such as searching over all = synonyms, confirming the selected concept using the preferred term or fully= specified name, ordering value lists effectively using an ordered referenc= e set, searching using navigation hierarchies, and constraining data entry = using subsets.
1 <= /sup> These techniques can also help to reduce data entry errors by p= rohibiting invalid input, helping the user to understand the correct meanin= g of the code selected, and ordering value lists in a clinically safe order= (e.g. ordering medications by strength, rather than alphabetically).
Diagnostic criteria and their application tends to vary widely according= to care setting, patient status and healthcare professional. The consisten= t ascertainment and recording of even common diagnoses, such as asthma and = myocardial infarction is often non-trivial. High quality prospective resear= ch studies require that diagnostic criteria for the condition being studied= are understood, rigorously applied and accurately documented. In routine c= linical practice doing this for potentially thousands of diagnoses in dozen= s of care settings is normally infeasible. Divergence and inconsistencies i= n criteria for diagnosis capture can undermine the validity of any conclusi= ons which may be drawn from analytics. SNOMED CT mitigates this issue by al= lowing the query author to choose a reliable aggregating concept from SNOME= D CT's extensive content.
Pick lists and constraints should be consistent with both clinical data = collection needs and analytic requirements and these should never be in con= flict. The presence or absence of particular concepts in value sets within = different applications can cause data collection to be inconsistent. SNOMED= CT mitigates this by allowing the query author to choose a reliable aggreg= ating concept.
Clinical data often undergoes a number of structural transformations and= code mappings prior to data analytics being performed, during the process = of preparing the data for messaging and/or loading into a data warehouse. I= n each of these transformations, care must be taken to ensure that the qual= ity of the process is high, and that there is no incremental shift in the c= linical meaning of the data. For example, mapping local codes to an alterna= te code system using non-equivalence maps (e.g. narrow to broad or broad to= narrow) will change the clinical meaning of these codes to some degree. An= y changes that effect the clinical meaning of the data may have an impact o= n the quality of data analytics. SNOMED CT helps to mitigate this by suppor= ting the representation of equivalence maps, which can be used when the use= case requires.
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1 | SNOMED CT Search and Data Entry Guide, 2= 014, http://snomed.org/search. |