Journal club: Requiring clinical justification to override repeat imaging decision support: impact on CT use.

OBJECTIVE: The purpose of this study was to determine the impact of requiring clinical justification to override decision support alerts on repeat use of CT. SUBJECTS AND METHODS: This before and after intervention study was conducted at a 793-bed tertiary hospital with computerized physician order entry and clinical decision support systems. When a CT order is placed, decision support alerts the orderer if the patient's same body part has undergone CT within the past 90 days. The study cohort included all 28,420 CT orders triggering a repeat alert in 2010. The intervention required clinical justification, selected from a predetermined menu, to override repeat CT decision support alerts to place a CT order; otherwise the order could not be placed and was dropped. The primary outcome, dropped repeat CT orders, was analyzed using three methods: chi-square tests to compare proportions dropped before and after intervention; multiple logistic regression tests to control for orderer, care setting, and patient factors; and statistical process control for temporal trends. RESULTS: The repeat CT order drop rate had an absolute increase of 1.4%; 6.1% (682/11,230) before to 7.5% (1290/17,190) after intervention, which was a 23% relative change (7.5 - 6.1)/6.1 × 100 = 23%; p < 0.0001). Orders were dropped more often after intervention (odds ratio, 1.3; 95% CI, 1.1-1.4; p < 0.0001). Statistical control analysis supported the association between the increase in the drop rate with intervention rather than underlying trends. CONCLUSION: Adding a requirement for clinical justification to override alerts modestly but significantly improves the impact of repeat CT decision support (23% relative change), with the overall effect of preventing one in 13 repeat CT orders.

Use of a web-based risk appraisal tool for assessing family history and lifestyle factors in primary care.

BACKGROUND: Primary care clinicians can play an important role in identifying individuals at increased risk of cancer, but often do not obtain detailed information on family history or lifestyle factors from their patients. OBJECTIVE: We evaluated the feasibility and effectiveness of using a web-based risk appraisal tool in the primary care setting. DESIGN: Five primary care practices within an academic care network were assigned to the intervention or control group. PARTICIPANTS: We included 15,495 patients who had a new patient visit or annual exam during an 8-month period in 2010-2011. INTERVENTION: Intervention patients were asked to complete a web-based risk appraisal tool on a laptop computer immediately before their visit. Information on family history of cancer was sent to their electronic health record (EHR) for clinicians to view; if accepted, it populated coded fields and could trigger clinician reminders about colon and breast cancer screening. MAIN MEASURES: The main outcome measure was new documentation of a positive family history of cancer in coded EHR fields. Secondary outcomes included clinician reminders about screening and discussion of family history, lifestyle factors, and screening. KEY RESULTS: Among eligible intervention patients, 2.0% had new information on family history of cancer entered in the EHR within 30 days after the visit, compared to 0.6% of eligible control patients (adjusted odds ratio = 4.3, p = 0.03). There were no significant differences in the percent of patients who received moderate or high risk reminders for colon or breast cancer screening. CONCLUSIONS: Use of this tool was associated with increased documentation of family history of cancer in the EHR, although the percentage of patients with new family history information was low in both groups. Further research is needed to determine how risk appraisal tools can be integrated with workflow and how they affect screening and health behaviors.

A computerized aid to support smoking cessation treatment for hospital patients.

BACKGROUND: Hospital-based interventions promote smoking cessation after discharge. Strategies to deliver these interventions are needed, especially now that providing smoking cessation advice or treatment, or both, to inpatient smokers is a publicly reported quality-of-care measure for US hospitals. OBJECTIVE: To assess the effect of adding a tobacco order set to an existing computerized order-entry system used to admit Medicine patients to 1 hospital. DESIGN: Pre-post study. MEASUREMENTS AND MAIN RESULTS: Proportion of admitted patients who had smoking status identified, a smoking counselor consulted, or nicotine replacement therapy (NRT) ordered during 4 months before and after the change. In 4 months after implementation, the order set was used with 76% of Medicine admissions, and a known smoking status was recorded for 81% of these patients. The intervention increased the proportion of admitted patients who were referred for smoking counseling (0.8 to 2.1%) and had NRT ordered (1.6 to 2.5%) (p < .0001 for both). Concomitantly, the hospital's performance on the smoking cessation quality measure improved. CONCLUSIONS: Adding a brief tobacco order set to an existing computerized order-entry system increased a hospital's provision of evidence-based tobacco treatment and helped to improve its performance on a publicly reported quality measure. It provides a model for US hospitals seeking to improve their quality of care for inpatients.

Implementation of a CKD checklist for primary care providers.

BACKGROUND AND OBJECTIVES: CKD is associated with significant morbidity, mortality, and financial burden. Practice guidelines outlining CKD management exist, but there is limited application of these guidelines. Interventions to improve CKD guideline adherence have been limited. This study evaluated a new CKD checklist (a tool outlining management guidelines for CKD) to determine whether implementation in an academic primary care clinic improved adherence to guidelines. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: During a 1-year period (August 2012-August 2013), a prospective study was conducted among 13 primary care providers (PCPs), four of whom were assigned to use a CKD checklist incorporated into the electronic medical record during visits with patients with CKD stages 1-4. All providers received education regarding CKD guidelines. The intervention and control groups consisted of 105 and 263 patients, respectively. Adherence to CKD management guidelines was measured. RESULTS: A random-effects logistic regression analysis was performed to account for intra-group correlation by PCP assignment and adjusted for age and CKD stage. CKD care improved among patients whose PCPs were assigned to the checklist intervention compared with controls. Patients in the CKD checklist group were more likely than controls to have appropriate annual laboratory testing for albuminuria (odds ratio [OR], 7.9; 95% confidence interval [95% CI], 3.6 to 17.2), phosphate (OR, 3.5; 95% CI, 1.5 to 8.3), and parathyroid hormone (OR, 8.1; 95% CI, 4.8 to 13.7) (P<0.001 in all cases). Patients in the CKD checklist group had higher rates of achieving a hemoglobin A1c target<7% (OR, 2.7; 95% CI, 1.4 to 5.1), use of an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker (OR, 2.1; 95% CI, 1.0 to 4.2), documentation of avoidance of nonsteroidal anti-inflammatory drugs (OR, 41.7; 95% CI, 17.8 to 100.0), and vaccination for annual influenza (OR, 2.1; 95% CI, 1.1 to 4.0) and pneumococcus (OR, 4.7; 95% CI, 2.6 to 8.6) (P<0.001 in all cases). CONCLUSIONS: Implementation of a CKD checklist significantly improved adherence to CKD management guidelines and delivery of CKD care.

Impact of IT-enabled intervention on MRI use for back pain.

BACKGROUND: The purpose of this study was to examine the impact of a multifaceted, clinical decision support (CDS)-enabled intervention on magnetic resonance imaging (MRI) use in adult primary care patients with low back pain. METHODS: After a baseline observation period, we implemented a CDS targeting lumbar-spine MRI use in primary care patients with low back pain through our computerized physician order entry, as well as 2 accountability tools: mandatory peer-to-peer consultation when test utility was uncertain and quarterly practice pattern variation reports to providers. Our primary outcome measure was rate of lumbar-spine MRI use. Secondary measures included utilization of MRI of any body part, comparing it with that of a concurrent national comparison, as well as proportion of lumbar-spine MRI performed in the study cohort that was adherent to evidence-based guideline. Chi-squared, t-tests, and logistic regression were used to assess pre- and postintervention differences. RESULTS: In the study cohort preintervention, 5.3% of low back pain-related primary care visits resulted in lumbar-spine MRI, compared with 3.7% of visits postintervention (P <.0001, adjusted odds ratio 0.68). There was a 30.8% relative decrease (6.5% vs 4.5%, P <.0001, adjusted odds ratio 0.67) in the use of MRI of any body part by the primary care providers in the study cohort. This difference was not detected in the control cohort (5.6% vs 5.3%, P = .712). In the study cohort, adherence to evidence-based guideline in the use of lumbar-spine MRI increased from 78% to 96% (P = .0002). CONCLUSIONS: CDS and associated accountability tools may reduce potentially inappropriate imaging in patients with low back pain.

Adoption and meaningful use of computerized physician order entry with an integrated clinical decision support system for radiology: ten-year analysis in an urban teaching hospital.

PURPOSE: The aim of this study was to assess whether an integrated imaging computerized physician order entry (CPOE) system with embedded decision support for imaging can be accepted clinically. METHODS: The study was performed in a health care delivery network with an affiliated academic hospital. After pilot testing and user feedback, a Web-enabled CPOE system with embedded imaging decision support was phased into clinical use between 2000 and 2010 across outpatient, emergency department, and inpatient settings. The primary outcome measure was meaningful use, defined as the proportion of imaging studies performed with orders electronically created (EC) or electronically signed by an authorized provider. The secondary outcome measure was adoption, defined as the proportion of imaging studies that were ordered electronically, irrespective of who entered the order in the CPOE system. Univariate and multivariate regression analyses were performed to estimate trends and the significance of practice settings, examination modality, and body part to outcome measures. Chi-square statistics were used to assess differences across specialties. RESULTS: A total of 4.1 million imaging studies were performed during the study period. From 2000 to 2010, significant increases in meaningful use (for EC studies, from 0.4% to 61.9%; for electronically signed studies, from 0.4% to 92.2%; P < .005) and the adoption of CPOE (from 0.5% to 94.6%, P < .005) were observed. The use of EC studies was greatest in the emergency department and inpatient settings. Meaningful use varied across specialties; surgical subspecialties had the lowest rates of EC studies. CONCLUSIONS: Imaging CPOE with embedded decision support integrated into the IT infrastructure of the health care enterprise and clinicians' workflow can be broadly accepted clinically.

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

BACKGROUND: Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. OBJECTIVE: To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. STUDY DESIGN AND METHODS: We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. RESULTS: Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. CONCLUSION: We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

Clinician characteristics and use of novel electronic health record functionality in primary care.

BACKGROUND: Conventional wisdom holds that older, busier clinicians who see complex patients are less likely to adopt and use novel electronic health record (EHR) functionality. METHODS: To compare the characteristics of clinicians who did and did not use novel EHR functionality, we conducted a retrospective analysis of the intervention arm of a randomized trial of new EHR-based tobacco treatment functionality. RESULTS: The novel functionality was used by 103 of 207 (50%) clinicians. Staff physicians were more likely than trainees to use the functionality (64% vs 37%; p<0.001). Clinicians who graduated more than 10 years previously were more likely to use the functionality than those who graduated less than 10 years previously (64% vs 42%; p<0.01). Clinicians with higher patient volumes were more likely to use the functionality (lowest quartile of number of patient visits, 25%; 2nd quartile, 38%; 3rd quartile, 65%; highest quartile, 71%; p<0.001). Clinicians who saw patients with more documented problems were more likely to use the functionality (lowest tertile of documented patient problems, 38%; 2nd tertile, 58%; highest tertile, 54%; p=0.04). In multivariable modeling, independent predictors of use were the number of patient visits (OR 1.2 per 100 additional patients; 95% CI 1.1 to 1.4) and number of documented problems (OR 2.9 per average additional problem; 95% CI 1.4 to 6.1). CONCLUSIONS: Contrary to conventional wisdom, clinically busier physicians seeing patients with more documented problems were more likely to use novel EHR functionality.

An electronic health record-based intervention to improve tobacco treatment in primary care: a cluster-randomized controlled trial.

BACKGROUND: To improve the documentation and treatment of tobacco use in primary care, we developed and implemented a 3-part electronic health record enhancement: (1)smoking status icons, (2) tobacco treatment reminders, and (3) a Tobacco Smart Form that facilitated the ordering of medication and fax and e-mail counseling referrals. METHODS: We performed a cluster-randomized controlled trial of the enhancement in 26 primary care practices between December 19, 2006, and September 30, 2007. The primary outcome was the proportion of documented smokers who made contact with a smoking cessation counselor. Secondary outcomes included coded smoking status documentation and medication prescribing. RESULTS: During the 9-month study period, 132 630 patients made 315 962 visits to study practices. Coded documentation of smoking status increased from 37% of patients to 54% (+17%) in intervention practices and from 35% of patients to 46% (+11%) in control practices (P < .001 for the difference in differences). Among the 9589 patients who were documented smokers at the start of the study, more patients in the intervention practices were recorded as nonsmokers by the end of the study (5.3% vs 1.9% in control practices; P < .001). Among 12 207 documented smokers, more patients in the intervention practices made contact with a cessation counselor (3.9% vs 0.3% in control practices; P < .001). Smokers in the intervention practices were no more likely to be prescribed smoking cessation medication (2% vs 2% in control practices; P = .40). CONCLUSION: This electronic health record-based intervention improved smoking status documentation and increased counseling assistance to smokers but not the prescription of cessation medication.

The association between patient-reported receipt of tobacco intervention at a primary care visit and smokers' satisfaction with their health care.

U.S. Public Health Service (USPHS) clinical guidelines for tobacco treatment recommend that providers routinely counsel smokers using a five-step algorithm (5A's): ask about tobacco use, advise smokers to quit, assess interest in quitting, assist with treatment, and arrange follow-up. A potential barrier to compliance is providers' concern that addressing smoking might alienate smokers, especially those not ready to quit. A survey was mailed to 1,985 patients seen at one of eight Boston-area primary care practices from January 1 to March 31, 2003, and identified as smokers by chart review. The survey assessed respondents' receipt of the 5A's at their visit and their satisfaction with the provider's tobacco treatment and with their overall health care. We used multivariable logistic regression models to assess the association between satisfaction with care and patient-reported receipt of each 5A step, adjusted for age, sex, education, race, health status, smoking intensity, readiness to quit, and length of relationship with provider. Of 1,160 respondents (58% response rate), 765 reported that they smoked at the time of the visit. They reported high levels of satisfaction with their tobacco-related care and overall care. Patient-reported receipt of each 5A step was significantly associated with greater patient satisfaction with tobacco-related care and with overall health care, even after adjusting for a smoker's readiness to quit smoking. Satisfaction with overall health care increased as counseling intensity increased. Patient reports of smoking cessation interventions delivered during primary care practice are associated with greater patient satisfaction with their health care, even among smokers not ready to quit. Providers can follow USPHS guidelines with smokers without fear of alienating those not yet considering quitting.

Measuring provider adherence to tobacco treatment guidelines: a comparison of electronic medical record review, patient survey, and provider survey.

An accurate method of measuring primary care providers' tobacco counseling actions is needed for monitoring adherence to clinical practice guidelines. We compared three methods of measuring providers' tobacco counseling practices: electronic medical record (EMR) review, patient survey, and provider survey. We mailed a survey to 1,613 smokers seen by 114 Boston-area primary care providers during a 2-month period to assess what tobacco counseling actions had occurred at the visit (N = 766; 47% response rate). Smokers' reports were compared with the EMR and with their providers' self-reported usual tobacco counseling practices, derived from a provider survey (N = 110; 96% response rate). Patients reported receiving each counseling action more frequently than providers documented it in the EMR. Agreement between the patient survey and the EMR was poor for all 5A steps (kappa statistic = 0.01-0.22). Providers reported that they often or always performed each 5A action at a higher rate than indicated by EMR or patient report. However, providers who said they often or always performed individual 5A steps did not have consistently higher mean rates of EMR documentation or patient report than those who said they performed the 5A's less frequently. Little agreement was found among the three methods of measuring primary care providers' tobacco counseling actions. Implementing an EMR does not necessarily improve providers' documentation of tobacco interventions, but EMR adaptations that would standardize provider documentation of tobacco counseling might make the EMR a more reliable tool for monitoring providers' delivery of tobacco treatment services.