In this large retrospective cohort study we have compared medication intensification information obtained from narrative and structured data sources in the EMR. Frequency of documented medication intensification were relatively low in both sources −0.060 and 0.076/mo from the notes and structured medication data, respectively—consistent with the previously reported rates of medication intensification for other hypertensive populations. 23,29
While both sources had a similar number of documented intensifications, less than a third of all intensifications were recorded in both structured and narrative data. Concordance between the two information sources increased slightly over the course of the study, possibly reflecting the users' level of comfort and familiarity with the EMR application. However, even by the end of the study less than 40% of all anti-hypertensive medication intensification events were documented in both narrative and structured data.
Several reasons could be contributing to this large discrepancy. Recording all changes to the patient's medications in the narrative notes is required from physicians for billing purposes. 36
At the same time, entering medication information into structured records in the EMR can be time-consuming, particularly for less experienced users. Some physicians may therefore view recording medications in structured lists as duplicative work, leading to avoidance as an expected coping behavior. Even when electronic prescribing is mandatory (as it was in some of the practices during a part of the study period), it is possible for physicians to circumvent the EMR, for example, by calling the pharmacy or instructing the patient to change the medication dose without actually changing the prescription. By contrast, medication changes initiated outside of a face-to-face physician–patient encounter (e.g., by telephone or e-mail) may never be recorded in narrative notes. This possible explanation is supported by the large fraction of medication intensifications in our structured data that were entered on the days when there was no documented physician–patient encounter in the EMR. Anecdotally, as entering prescriptions through EMR rather than on paper has become mandatory in some practices at our institution, compliance has improved. That may have been one of the reasons for a small annual increase in concordance between narrative and structured medication information. However, large discrepancies still remain. Further studies are needed to elucidate the reasons for these discrepancies and identify ways of eliminating them.
Both narrative and structured EMR records have strengths and weaknesses as possible sources of medication information. Structured records may contain more complete information about a particular medication as the users are forced to enter all elements of the prescription. Electronic medication records are also easier to process computationally than narrative text. Consequently a transition towards a greater number of structured data entry options in the EMR has been advocated. 37
By contrast, narrative text may contain other contextual information that is important for interpretation of the provider action (e.g., about the patient's medication adherence). 38,39
As a result, most EMRs contain a combination of narrative and structured data. 40,41
Furthermore, not all prescriptions may correctly reflect the actual dosing of the medication. For example, it is not uncommon for physicians to prescribe a higher strength of an expensive medication to lower the patient's costs, since the difference in the cost of two different formulations of the same medication is frequently less than the difference in the amount of the chemical ingredient.
Due to the shortcomings of these and other data sources, there is no single gold standard information source for patients' medication information. As a result, concurrent validation (which shows a correlation with an existing test, such as manual chart review 42
) does not provide comprehensive verification of treatment intensification data obtained from either narrative or structured data. To complement this approach we therefore employed predictive validation 42
against the patients' clinical outcomes. It has been well established that anti-hypertensive treatment intensification in real-life clinical environment leads to a decrease in blood pressure levels. 19,23
We were able to demonstrate that this held true for medication intensification information obtained from both narrative and structured EMR records. The relationship remained highly significant in a multivariate analysis that also included patient demographic information and a correction for intra-provider clustering. The deviation from the linear relationship between medication intensification and blood pressure changes observed for encounters with no intensification may have been due to clinical circumstances that rendered intensification inappropriate (e.g., medication nonadherence or acute disease).
To our knowledge, this is the first large-scale analysis that directly compares narrative and structured data in the EMR. It included several thousand patients from two large hospitals that care for patients from all socioeconomic strata. Digital entry of the notes was mandatory for all practices that used the EMR during the study period thus eliminating a potential selection bias. Finally, the data used in our study was validated not only against the original source (which may or may not have been correct itself) but also against the patients' clinical outcomes—a much higher standard that can be difficult to attain.
Our investigation has several limitations. It was restricted in scope to the patients of primary care physicians affiliated with two academic hospitals in Eastern Massachusetts that used an internally developed EMR; this could limit its generalizability. However, although the EMR used in the study was developed internally, its electronic prescribing and narrative note documentation features are similar to many of the commercially available products. The study was restricted to adult patients due to a limited number of pediatric patients with diabetes and hypertension treated at the two study hospitals. Consequently a separate study in children should be carried out to confirm our findings in this population. Patients with short-term (less than 1 mo) elevations of blood pressure were excluded. However, this restriction led to the exclusion of only 59 patients (c 1% of the total number of patients in the study). Therefore, it is unlikely to have significantly affected the study findings. Using individual periods of continuously elevated blood pressure rather than unique patients as the unit of analysis of correlation between treatment intensification and blood pressure may have led to a bias in favor of patients with multiple periods of elevated blood pressure. However, this approach led to a significant reduction in the noise level introduced by normotensive periods and the possible bias was addressed using hierarchical regression models. This retrospective study relied on documentation of relevant findings in the EMR, leading to a possible bias if the documentation was uneven with respect to the study outcomes. Electronic prescribing was not universally mandatory during the study leading to a possible selection bias; however, nearly 85% of the study patients had at least one structured medication record in the EMR. Electronic prescribing became mandatory at some of the practices during the study period which could have affected the study results. The dates when electronic prescribing became mandatory were not available and therefore quantitative analysis of these effects was not feasible. It was not possible to ascertain exactly when patients' blood pressure normalized, limiting the precision of our calculations of the rate of blood pressure change. We considered medication intensifications documented in both sources on the same day identical, even if we could not always establish that they referred to the same medication. However, we were able to establish the equivalence of the medications being intensified in over 90% of the cases. The remaining difference would have tended to bias our multivariate analysis of the relationship of medication intensification information from both sources and the patients' blood pressure towards the null hypothesis by inappropriately decreasing the intensification rate calculated from structured data.
We were not able to obtain pharmacy and/or insurance claims data to complement medication information from EMR records. It is possible that inclusion of these high-validity 43
sources could have helped to resolve some of the discrepancies in the EMR data observed in our study. In the future, point-of-care availability of claims and pharmacy medication records would likely be an important component of closed-loop medication information systems which would facilitate reconciliation of medication information between different data sources.