Letters intervention, residents were aware that albuterol neb treat- Analysis and interpretation of data: Moriates, Novelero, Khanna, Mourad. ments were more expensive than albuterol MDIs (82%, pre- Drafting of the manuscript: Moriates, Novelero. Critical revision of the manuscript for important intellectual content: Quinn, test; 94%, postintervention [P = .11]). Prior to the interven- Khanna, Mourad. tion 13 of the residents (26%) answered incorrectly that neb Statistical analysis: Khanna. treatments were more efficacious than MDIs, in contrast to only Obtained funding: Novelero. 1 resident (3%) following exposure to our intervention (P < .01). Administrative, technical, and material support: Novelero, Quinn, Mourad. Study supervision: Khanna, Mourad. At baseline, none of the residents agreed that “patients re- Published Online: July 22, 2013. ceive adequate inpatient MDI teaching”; however, this rate im- doi:10.1001/jamainternmed.2013.9002. proved to 16% after the first 2 months of implementation Conflict of Interest Disclosures: None reported. (P < .01). Additional Contributions: Theodore Omachi, MD, MBA (Department of Medicine, University of California, San Francisco), and Sumant Ranji, MD Discussion | Our multifaceted intervention was associated (Department of Medicine, University of California, San Francisco), contributed with a simultaneous decrease in unnecessary neb treat- to the design and implementation of this project. They did not receive compensation. ments, an increase in evidence-based resident physician 1. Turner MO, Patel A, Ginsburg S, FitzGerald JM. Bronchodilator delivery in knowledge, and potentially an improvement in MDI patient acute airflow obstruction: a meta-analysis. Arch Intern Med. 1997;157(15):1736- education. This concurrent improvement in quality of care 1744. with a decrease in cost maximizes the “value equation” 2. Dolovich MB, Ahrens RC, Hess DR, et al; American College (defined as quality divided by costs). The approximately of Chest Physicians; American College of Asthma, Allergy, and 50% decrease in nebs following our intervention highlights Immunology. Device selection and outcomes of aerosol therapy: evidence-based guidelines: American College of Chest the degree of wasteful usage of this resource-intensive Physicians/American College of Asthma, Allergy, and Immunology. Chest. therapy previously on our pilot medical ward. Reducing 2005;127(1):335-371. inappropriate nebs represents a straightforward way for 3. Mandelberg A, Chen E, Noviski N, Priel IE. Nebulized wet aerosol treatment institutions to reduce health care costs through a simple in emergency department: is it essential? comparison with large spacer device intervention. for metered-dose inhaler. Chest. 1997;112(6):1501-1505. Our study has some limitations. Owing to the nature of 4. Press VG, Arora VM, Shah LM, et al. Misuse of respiratory inhalers in hospitalized patients with asthma or COPD. J Gen Intern Med. our intervention and the significant crossover of our physi- 2011;26(6):635-642. cians, RTs, and nurses, it was not possible to create a control group at our medical center during this pilot study. Also, our financial model may overestimate our cost savings since RT Documentation and Diagnosis of Overweight time is a semifixed cost and our intervention has not yet led and Obesity in Electronic Health Records to a decrease in actual RT full-time equivalents. However, of Adult Primary Care Patients RT daily staffing is based on volume at our large hospital; Almost 69% of US adults are either overweight or obese thus, if the project is successfully scaled medical center– (body mass index [BMI], calculated as weight in kilograms wide, then it would likely result in a decrease in daily divided by height in meters squared, ≥25),1 yet clinicians RT staffing. Currently, this saved time is being repurposed often fail to diagnose overweight and obesity or discuss for our RTs to perform other important job duties at our weight management with their patients.2-6 Many clinicians hospital, such as MDI training and smoking cessation use electronic health records (EHRs), and adoption of EHRs counseling. has been increasing since the introduction of the Health In conclusion, our pilot study illustrates that a multifac- Information Technology for Economic and Clinical Health eted effort may be successful in dramatically decreasing the (HITECH) Act in 2009.7 Electronic recording of vital signs— overuse of neb therapies on an inpatient medicine service. Re- including height, weight, and BMI—is now one of the ducing utilization of these resource intensive and unneces- requirements for achieving “meaningful use” of EHRs,8 but sary treatments may provide an ideal target for improving few studies have examined rates of BMI documentation and health care value. diagnosis of overweight and obesity in EHR data. We con- ducted a retrospective study to examine these rates in the Christopher Moriates, MD EHRs of adult primary care patients before the passing of Maria Novelero, MA, MPA the HITECH Act in 2009. Kathryn Quinn, MPH Raman Khanna, MD Methods | We evaluated patients at 25 primary care practices Michelle Mourad, MD within a large academic care network in Boston, Massachu- setts. We included adult patients (≥18 years) who had at Author Affiliations: Department of Medicine, University of California, San least 2 visits with the same clinician between 2004 and Francisco, San Francisco (Moriates, Novelero, Quinn, Khanna, Mourad). 2008 and were not pregnant at the time of the visit. The Corresponding Author: Christopher Moriates, MD, Department of Medicine, study was approved by the Partners Human Research University of California, San Francisco, 505 Parnassus Ave, M1287, San Committee. Francisco, CA 94143-0131 ([email protected]). Data were extracted from coded fields in the EHR. The Author Contributions: Study concept and design: Moriates, Novelero, Quinn, Mourad. primary outcome was documentation of at least 1 BMI in the Acquisition of data: Moriates, Novelero. appropriate coded EHR field at any time during the study 1648 JAMA Internal Medicine September 23, 2013 Volume 173, Number 17 jamainternalmedicine.com Downloaded From: https://jamanetwork.com/ on 09/29/2021 Letters Table 1. Documentation of Body Mass Index (BMI) in the Electronic Health Records of 219 356 Adult Primary Care Patients No. in ≥1 BMI, Adjusted OR Characteristic Group No. (%) (95% CI) P Value Overall 144 522 (65.9) Patient Characteristics Age at first visit, y 18-29 42 170 27 934 (66.2) 1 [Reference] 30-39 46 382 29 851 (64.4) 0.93 (0.90-0.97) 40-49 45 156 30 427 (67.4) 1.04 (1.00-1.08) <.001 50-59 38 822 26 438 (68.1) 1.00 (0.96-1.04) 60-69 24 877 16 729 (67.3) 0.94 (0.89-0.99) ≥70 21 949 13 143 (59.9) 0.60 (0.56-0.63) Sex Male 81 742 48 233 (59.0) 1 [Reference] <.001 Female 137 614 96 289 (70.0) 1.45 (1.41-1.48) Race/ethnicity White 145 391 96 081 (66.1) 1 [Reference] Black 17 814 11 851 (66.5) 1.01 (0.97-1.05) Hispanic/Latino 29 432 19 935 (67.7) 1.05 (1.00-1.09) <.001 Asian 8885 6033 (67.9) 0.97 (0.92-1.03) Other or missing 17 834 10 622 (59.6) 0.84 (0.81-0.88) Primary insurance Private 167 479 111 906 (66.8) 1 [Reference] Medicare 36 734 23 759 (64.7) 0.94 (0.90-0.98) <.001 Medicaid 5764 3979 (69.0) 0.93 (0.87-1.00) No insurance or self-pay 9379 4878 (52.0) 0.64 (0.61-0.68) Visits, No. 2-5 75 868 41 572 (54.8) 1 [Reference] 6-9 52 940 35 002 (66.1) 1.87 (1.81-1.92) <.001 10-14 38 295 27 491 (71.8) 2.78 (2.68-2.87) ≥15 52 253 40 457 (77.4) 4.66 (4.50-4.83) Obesity-related comorbidities, No. 0 109 051 65 300 (59.9) 1 [Reference] 1 52 549 36 603 (69.7) 1.34 (1.30-1.38) <.001 2 30 213 21 844 (72.3) 1.48 (1.42-1.53) ≥3 27 543 20 775 (75.4) 1.73 (1.66-1.80) Clinician Characteristicsa Age, y <30 20 336 14 042 (69.1) 1 [Reference] 30-39 76 896 52 394 (68.1) 1.04 (0.88-1.24) 40-49 59 452 39 948 (67.2) 1.14 (0.91-1.41) .23 50-59 49 581 30 319 (61.2) 1.10 (0.88-1.37) ≥60 12 462 7475 (60.0) 0.77 (0.52-1.15) Sex Male 100 487 60 247 (60.0) 1 [Reference] .01 Female 118 869 84 275 (70.9) 1.20 (1.04-1.38) Abbreviations: NP, nurse practitioner; Type OR, odds ratio; PA, physician Staff physician 188 273 124 744 (66.3) 1 [Reference] assistant. a NP or PA 20 307 13 058 (64.3) 0.88 (0.84-0.92) <.001 Counts refer to the number of patients who had visits with clinicians Resident or fellow 10 776 6720 (62.4) 1.02 (0.95-1.08) with these characteristics. period. Body mass index is calculated if patients have both patients with at least 1 BMI of at least 25 (overweight) or at height and weight; once a height has been entered, it is car- least 30 (obese), we also examined whether they had a diag- ried forward and used in subsequent calculations. Among nosis of “overweight,” “obesity,” “weight gain,” or “weight jamainternalmedicine.com JAMA Internal Medicine September 23, 2013 Volume 173, Number 17 1649 Downloaded From: https://jamanetwork.com/ on 09/29/2021 Letters Table 2. Diagnosis of Overweight and Obesity Among 98 762 Adult Primary Care Patients With BMI of at Least 25 Diagnosis of Overweight/Obesity No. in on Problem List, Adjusted OR Characteristic Group No.