Modelling Conditions and Health Care Processes in Electronic Health Records: An Application to Severe Mental Illness with the Clinical Practice Research Datalink

Abstract

Background: The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example.

Methods: We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are selected. The final code-list is then used to identify patients.

Results: We identified 270 Read codes linked to SMI and used them to identify cases in the database. We observed that our approach identified cases that would have been missed with a simpler approach using SMI registers defined within the UK Quality and Outcomes Framework.

Conclusion: We described a framework for researchers of Electronic Health Records databases, for identifying patients with a particular condition or matching certain clinical criteria. The method is invariant to coding system or database and can be used with SNOMED CT, ICD or other medical classification code-lists.

Fig 1. Process flowchart The first step is the definition and delineation of the condition.

Within UK primary care, it is also important to consider whether the condition is one of those incentivised under the QOF, since a specific set of business rules will be available for use as a starting point. An expert panel, consisting of clinicians with experience in the particular condition and primary care clinical systems, should suggest a set of search key-words, key-phrases and codes (QOF-specific or not). In the context of a primary care database like the CPRD, the search is focused on two lookup files which contain codes and descriptions for clinical events (mainly diagnoses and referrals) and products (mainly drugs). To facilitate the search we have created pcdsearch a Stata/R command that can automate this aspect of the process, implementing advanced search rules which we describe in the next section.

Fig 2. Search terms used with pcdsearch to obtain the intermediate SMI code-list*.

* QOF codes are not directly used in the search algorithm but are used to inform the code-stubs to be used.

Fig 3. Frequencies for code categories in the final Read code-list.

Fig 4. SMI Prevalence (top) and incidence (bottom) rates over time, using QOF and conservative code lists.

Fig 5. SMI Prevalence rates over time using QOF and conservative code lists, for men (top) and women (bottom).