Person:

Linder, Jeffrey A

Background: Ambulatory antibiotic prescribing contributes to the development of antibiotic resistance and increases societal costs. Here, we estimate the hidden societal cost of antibiotic resistance per antibiotic prescribed in the United States. Methods: In an exploratory analysis, we used published data to develop point and range estimates for the hidden societal cost of antibiotic resistance (SCAR) attributable to each ambulatory antibiotic prescription in the United States. We developed four estimation methods that focused on the antibiotic-resistance attributable costs of hospitalization, second-line inpatient antibiotic use, second-line outpatient antibiotic use, and antibiotic stewardship, then summed the estimates across all methods. Results: The total SCAR attributable to each ambulatory antibiotic prescription was estimated to be $13 (range: $3–$95). The greatest contributor to the total SCAR was the cost of hospitalization ($9; 69 % of the total SCAR). The costs of second-line inpatient antibiotic use ($1; 8 % of the total SCAR), second-line outpatient antibiotic use ($2; 15 % of the total SCAR) and antibiotic stewardship ($1; 8 %). This apperars to be an error.; of the total SCAR) were modest contributors to the total SCAR. Assuming an average antibiotic cost of $20, the total SCAR attributable to each ambulatory antibiotic prescription would increase antibiotic costs by 65 % (range: 15–475 %) if incorporated into antibiotic costs paid by patients or payers. Conclusions: Each ambulatory antibiotic prescription is associated with a hidden SCAR that substantially increases the cost of an antibiotic prescription in the United States. This finding raises concerns regarding the magnitude of misalignment between individual and societal antibiotic costs.

Background: Guidelines and performance measures recommend avoiding antibiotics for acute cough/acute bronchitis and presume visits are straightforward with simple diagnostic decision-making. We evaluated clinician-assigned diagnoses, diagnostic uncertainty, and antibiotic prescribing for acute cough visits in primary care. Methods: We conducted a retrospective analysis of acute cough visits – cough lasting ≤21 days in adults 18–64 years old without chronic lung disease – in a primary care practice from March 2011 through June 2012. Results: Of 56,301 visits, 962 (2%) were for acute cough. Clinicians diagnosed patients with 1, 2, or ≥ 3 cough-related diagnoses in 54%, 35%, and 11% of visits, respectively. The most common principal diagnoses were upper respiratory infection (46%), sinusitis (10%), acute bronchitis (9%), and pneumonia (8%). Clinicians prescribed antibiotics in 22% of all visits: 65% of visits with antibiotic-appropriate diagnoses and 4% of visits with non-antibiotic-appropriate diagnoses. Clinicians expressed diagnostic uncertainty in 16% of all visits: 43% of visits with antibiotic-appropriate diagnoses and 5% of visits with non-antibiotic-appropriate diagnoses. Clinicians expressed uncertainty more often when prescribing antibiotics than when not prescribing antibiotics (30% vs. 12%; p < 0.001). As the number of visit diagnoses increased from 1 to 2 to ≥ 3, clinicians were more likely to express diagnostic uncertainty (5%, 25%, 40%, respectively; p < 0.001) and prescribe antibiotics (16%, 25%, 41%, respectively; p < 0.001). Conclusions: Acute cough may be more complex and have more diagnostic uncertainty than guidelines and performance measures presume. Efforts to reduce antibiotic prescribing for acute cough should address diagnostic complexity and uncertainty that clinicians face.

Background: Clinicians prescribe antibiotics to over 65% of adults with acute bronchitis despite guidelines stating that antibiotics are not indicated. Methods: To identify and understand primary care clinician perceptions about antibiotic prescribing for acute bronchitis, we conducted semi-structured interviews with 13 primary care clinicians in Boston, Massachusetts and used thematic content analysis. Results: All the participants agreed with guidelines that antibiotics are not indicated for acute bronchitis and felt that clinicians other than themselves were responsible for overprescribing. Barriers to guideline adherence included 6 themes: (1) perceived patient demand, which was the main barrier, although some clinicians perceived a recent decrease; (2) lack of accountability for antibiotic prescribing; (3) saving time and money; (4) other clinicians’ misconceptions about acute bronchitis; (5) diagnostic uncertainty; and (6) clinician dissatisfaction in failing to meet patient expectations. Strategies to decrease inappropriate antibiotic prescribing included 5 themes: (1) patient educational materials; (2) quality reporting; (3) clinical decision support; (4) use of an over-the-counter prescription pad; and (5) pre-visit triage and education by nurses to prevent visits. Conclusions: Clinicians continued to cite patient demand as the main reason for antibiotic prescribing for acute bronchitis, though some clinicians perceived a recent decrease. Clinicians felt that other clinicians were responsible for inappropriate antibiotic prescribing and that better pre-visit triage by nurses could prevent visits and change patients’ expectations. Electronic supplementary material The online version of this article (doi:10.1186/s12875-014-0194-5) contains supplementary material, which is available to authorized users.

Background: Inappropriate antibiotic prescribing for nonbacterial infections leads to increases in the costs of care, antibiotic resistance among bacteria, and adverse drug events. Acute respiratory infections (ARIs) are the most common reason for inappropriate antibiotic use. Most prior efforts to decrease inappropriate antibiotic prescribing for ARIs (e.g., educational or informational interventions) have relied on the implicit assumption that clinicians inappropriately prescribe antibiotics because they are unaware of guideline recommendations for ARIs. If lack of guideline awareness is not the reason for inappropriate prescribing, educational interventions may have limited impact on prescribing rates. Instead, interventions that apply social psychological and behavioral economic principles may be more effective in deterring inappropriate antibiotic prescribing for ARIs by well-informed clinicians. Methods/design The Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections (BEARI) Trial is a multisite, cluster-randomized controlled trial with practice as the unit of randomization. The primary aim is to test the ability of three interventions based on behavioral economic principles to reduce the rate of inappropriate antibiotic prescribing for ARIs. We randomized practices in a 2 × 2 × 2 factorial design to receive up to three interventions for non-antibiotic-appropriate diagnoses: 1) Accountable Justifications: When prescribing an antibiotic for an ARI, clinicians are prompted to record an explicit justification that appears in the patient electronic health record; 2) Suggested Alternatives: Through computerized clinical decision support, clinicians prescribing an antibiotic for an ARI receive a list of non-antibiotic treatment choices (including prescription options) prior to completing the antibiotic prescription; and 3) Peer Comparison: Each provider’s rate of inappropriate antibiotic prescribing relative to top-performing peers is reported back to the provider periodically by email. We enrolled 269 clinicians (practicing attending physicians or advanced practice nurses) in 49 participating clinic sites and collected baseline data. The primary outcome is the antibiotic prescribing rate for office visits with non-antibiotic-appropriate ARI diagnoses. Secondary outcomes will examine antibiotic prescribing more broadly. The 18-month intervention period will be followed by a one year follow-up period to measure persistence of effects after interventions cease. Discussion The ongoing BEARI Trial will evaluate the effectiveness of behavioral economic strategies in reducing inappropriate prescribing of antibiotics. Trials registration ClinicalTrials.gov: NCT01454947

Background: Chronic kidney disease (CKD) is increasingly common and under-recognized in primary care clinics, leading to low rates of stage-appropriate monitoring and treatment. Our objective was to determine whether electronic problem list documentation of CKD is associated with monitoring and treatment. Methods: This is a cross-sectional observational study of patients with stage 3 or 4 CKD, defined as two past estimated glomerular filtration rates (eGFR) 15-60 mL/min/1.73 m2 separated by 90 days and collected between 2007-2008. We examined the association of problem list documentation with: 1) serum eGFR monitoring test, 2) urine protein or albumin monitoring test, 3) an angiotensin converting enzyme inhibitor or angiotensin receptor blocker (ACE/ARB) prescription, 4) mean systolic blood pressure (BP), and 5) BP control. Results: Out of 3,149 patients with stage 3 or 4 CKD, only 16% of patients had CKD documented on the problem list. After adjustment for eGFR, gender, and race/ethnicity and after clustering by physician, problem list documentation of CKD was associated with serum eGFR testing (97% with problem list documentation vs. 94% without problem list documentation, p = 0.02) and urine protein testing (47% with problem list documentation vs. 40% without problem list documentation, p = 0.04). After adjustment, problem list documentation was not associated with ACE/ARB prescription, mean systolic BP, or BP control. Conclusions: Documentation of CKD on the electronic problem list is rare. Patients with CKD documentation have better stage-appropriate monitoring of the disease, but do not have higher rates of blood pressure treatment or better blood pressure control. Interventions aimed at increasing documentation of CKD on the problem list may improve stage-appropriate monitoring, but may not improve clinical outcomes.

Introduction: Practice-Based Research Networks (PBRNs) and health systems may provide timely, reliable data to guide the development and distribution of public health resources to promote healthy behaviors, such as quitting smoking. The objective of this study was to determine if PBRN data could be used to make neighborhood-level estimates of smoking prevalence. Methods: We estimated the smoking prevalence in 32 greater Boston neighborhoods (population = 877,943 adults) by using the electronic health record data of adults who in 2009 visited one of 26 Partners Primary Care PBRN practices (n = 77,529). We compared PBRN-derived estimates to population-based estimates derived from 1999–2009 Behavioral Risk Factor Surveillance System (BRFSS) data (n = 20,475). Results: The PBRN estimates of neighborhood smoking status ranged from 5% to 22% and averaged 11%. The 2009 neighborhood-level smoking prevalence estimates derived from the BRFSS ranged from 5% to 26% and averaged 13%. The difference in smoking prevalence between the PBRN and the BRFSS averaged −2 percentage points (standard deviation, 3 percentage points). Conclusion: Health behavior data collected during routine clinical care by PBRNs and health systems could supplement or be an alternative to using traditional sources of public health data.

Objective: To determine the diagnostic and triage accuracy of online symptom checkers (tools that use computer algorithms to help patients with self diagnosis or self triage). Design: Audit study. Setting: Publicly available, free symptom checkers. Participants: 23 symptom checkers that were in English and provided advice across a range of conditions. 45 standardized patient vignettes were compiled and equally divided into three categories of triage urgency: emergent care required (for example, pulmonary embolism), non-emergent care reasonable (for example, otitis media), and self care reasonable (for example, viral upper respiratory tract infection). Main outcome measures For symptom checkers that provided a diagnosis, our main outcomes were whether the symptom checker listed the correct diagnosis first or within the first 20 potential diagnoses (n=770 standardized patient evaluations). For symptom checkers that provided a triage recommendation, our main outcomes were whether the symptom checker correctly recommended emergent care, non-emergent care, or self care (n=532 standardized patient evaluations). Results: The 23 symptom checkers provided the correct diagnosis first in 34% (95% confidence interval 31% to 37%) of standardized patient evaluations, listed the correct diagnosis within the top 20 diagnoses given in 58% (55% to 62%) of standardized patient evaluations, and provided the appropriate triage advice in 57% (52% to 61%) of standardized patient evaluations. Triage performance varied by urgency of condition, with appropriate triage advice provided in 80% (95% confidence interval 75% to 86%) of emergent cases, 55% (47% to 63%) of non-emergent cases, and 33% (26% to 40%) of self care cases (P<0.001). Performance on appropriate triage advice across the 23 individual symptom checkers ranged from 33% (95% confidence interval 19% to 48%) to 78% (64% to 91%) of standardized patient evaluations. Conclusions: Symptom checkers had deficits in both triage and diagnosis. Triage advice from symptom checkers is generally risk averse, encouraging users to seek care for conditions where self care is reasonable.

Background: Clinicians frequently prescribe antibiotics inappropriately for acute respiratory infections (ARIs). Our objective was to test information technology-enabled behavioral interventions to reduce inappropriate antibiotic prescribing for ARIs in a randomized controlled pilot test trial. Methods: Primary care clinicians were randomized in a 2 × 2 × 2 factorial experiment with 3 interventions: 1) Accountable Justifications; 2) Suggested Alternatives; and 3) Peer Comparison. Beforehand, participants completed an educational module. Measures included: rates of antibiotic prescribing for: non-antibiotic-appropriate ARI diagnoses, acute sinusitis/pharyngitis, all other diagnoses/symptoms of respiratory infection, and all three ARI categories combined. Results: We examined 3,276 visits in the pre-intervention year and 3,099 in the intervention year. The antibiotic prescribing rate fell for non-antibiotic-appropriate ARIs (24.7 % in the pre-intervention year to 5.2 % in the intervention year); sinusitis/pharyngitis (50.3 to 44.7 %); all other diagnoses/symptoms of respiratory infection (40.2 to 25.3 %); and all categories combined (38.7 to 24.2 %; all p < 0.001). There were no significant relationships between any intervention and antibiotic prescribing for non-antibiotic-appropriate ARI diagnoses or sinusitis/pharyngitis. Suggested Alternatives was associated with reduced antibiotic prescribing for other diagnoses or symptoms of respiratory infection (odds ratio [OR], 0.62; 95 % confidence interval [CI], 0.44–0.89) and for all ARI categories combined (OR, 0.72; 95 % CI, 0.54–0.96). Peer Comparison was associated with reduced prescribing for all ARI categories combined (OR, 0.73; 95 % CI, 0.53–0.995). Conclusions: We observed large reductions in antibiotic prescribing regardless of whether or not study participants received an intervention, suggesting an overriding Hawthorne effect or possibly clinician-to-clinician contamination. Low baseline inappropriate prescribing may have led to floor effects. Trial Registration ClinicalTrials.gov: NCT01454960. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1715-8) contains supplementary material, which is available to authorized users.

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.