Table 2 Continued Name of measure and brief description Smoking cessation intervention Current smokers who received cessation interventions or counselling in the past 12 months Description of denominator (D) or numerator (N) data element (no of eligible charts) Recognized for quality measurement Location D N Documented current smokers (409) Smart form Smoking cessation intervention (129) Smart form 85 64.9 No 243 % 59.4 Research and applications Not recognized for quality measurement Location Social history Other Assessment Other Medications Scanned patient docs No 161 3 2 33 8 3 % 39.4 0.7 0.5 25.2 6.1 2.3 BMI, body mass index; BP, blood pressure; DM, diabetes mellitus; HPI, history of present illness; IVD, ischemic cardiovascular disease; LDL, low-density lipoprotein. relying on vitals, vaccinations, and medications had the highest proportion of information documented in structured ﬁelds recognized by the automated quality measures. The majority of diagnoses for chronic conditions such as diabetes (>91.4% across measures), hypertension (89.3%), ischemic cardiovascular disease (>78.8% across measures) and dyslipidemia (75.1%) were documented in the problem list, a structured ﬁeld used for automated quality measurement. Patient diagnoses not recog- nized for inclusion in the measure were recorded in the medical history, assessment, chief complaint, or history of present illness, sections that typically allow for free-text entries. Diagnostic orders or results for mammogram had the lowest proportion (10.7%) of data recorded in structured ﬁelds recog- nized for automated quality measurement. The majority of the information for breast cancer screening was found as scanned patient documents and diagnostic imaging; both sources of information are not amenable for automated electronic queries. Nearly half of the information for measures that require a laboratory test result, such as control of hemoglobin A1c and cholesterol, was documented in structured ﬁelds recognized for automated quality measurement (range 53.4e63.0%). Similarly, only half of the information regarding patient smoking status (53.4%) was recognized for automated quality measurement. With the exception of medications, vaccinations, and blood pressure readings, practices varied substantially in where they chose to document the data elements required for automated quality measurement (ﬁgure 1). In estimating the denominator loss due to unrecognizable documentation, the average practice missed half of the eligible patients for three of the 11 quality measuresdhemoglobin A1c control, cholesterol control, and smoking cessation intervention (table 3). No statistically signiﬁcant differences were observed between the e-chart and EHR automated quality measurement scores in the number of patients captured for the denominator for the remaining eight measures. Current EHR reporting would underreport practice numerators for six of the 11 measuresd hemoglobin A1c control, hemoglobin A1c screening, breast cancer screening, cholesterol control, cholesterol screening, and smoking status recorded. DISCUSSION As the nation continues to drive EHR adoption through signiﬁcant infusions of funding for health information technology infra- structure and support, and payment reform carries the promise of improved quality at lower cost, it is important and timely to assess the validity of EHR-derived clinical quality measures. Figure 1 Distribution of documentation and data elements recognized for quality measurement across 57 practices. EHR, electronic health record; IVD, ischemic cardiovascular disease; LDL, low-density lipoprotein. J Am Med Inform Assoc 2012;19:604e609. doi:10.1136/amiajnl-2011-000557 607