Publications by authors named "Elizabeth R Pfoh"

46 Publications

Patient Perspectives on Self-Monitoring of Blood Glucose When not Using Insulin: a Cross-sectional Survey.

J Gen Intern Med 2021 Aug 13. Epub 2021 Aug 13.

Center for Value-Based Care Research, Cleveland Clinic, 9500 Euclid Avenue, G10, Cleveland, OH, 44195, USA.

Background: Professional societies have recommended against use of self-monitoring blood glucose (SMBG) in non-insulin-treated type 2 diabetes (NITT2D) to control blood sugar levels, but patients are still monitoring.

Objective: To understand patients' motivation to monitor their blood sugar, and whether they would stop if their physician suggested it.

Design: Cross-sectional in-person and electronic survey conducted between 2018 and 2020.

Participants: Adults with type 2 diabetes not using insulin who self-monitor their blood sugar.

Main Measures: The survey included questions about frequency and reason for using SMBG, and the impact of SMBG on quality of life and worry. It also asked, "If your doctor said you could stop checking your blood sugar, would you?" We categorized patients based on whether they would stop. To identify the characteristics independently associated with desire to stop SMBG, we performed a logistic regression using backward stepwise selection.

Key Results: We received 458 responses. The common reasons for using SMBG included the doctor wanted the patient to check (67%), desire to see the number (65%), and desire to see if their medications were working (61%). Forty-eight percent of respondents stated that using SMBG reduced their worry about their diabetes and 61% said it increased their quality of life. Fifty percent would stop using SMBG if given permission. In the regression model, respondents who said that they check their blood sugar levels because "I was told to" were more likely to want to stop (AOR: 1.69, 95%CI: 1.11, 2.58). Those that used SMBG due to habit and to understand their diabetes better had lower odds of wanting to stop (AOR: 0.33, 95%CI: 0.18-0.62; AOR: 0.60, 95%CI: 0.39-0.93, respectively).

Conclusions: Primary care physicians should discuss patients' reasons for using SMBG and offer them the option of discontinuing.
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http://dx.doi.org/10.1007/s11606-021-07047-2DOI Listing
August 2021

Primary Care Health Care Use for Patients With Type 2 Diabetes During the COVID-19 Pandemic.

Diabetes Care 2021 09 14;44(9):e173-e174. Epub 2021 Jul 14.

Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland, OH.

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http://dx.doi.org/10.2337/dc21-0853DOI Listing
September 2021

Shared Medical Appointments and Prediabetes: The Power of the Group.

Ann Fam Med 2021 May-Jun;19(3):258-261

Center for Value-Based Care Research Cleveland Clinic, Cleveland, Ohio.

Shared medical appointments, which allow greater access to care and provide peer support, may be an effective treatment modality for prediabetes. We used a retrospective propensity-matched cohort analysis to compare patients attending a prediabetes shared medical appointment to usual care. Primary outcome was patient's weight change over 24 months. Secondary outcomes included change in hemoglobin A, low density lipoprotein, and systolic blood pressure. The shared medical appointments group lost more weight (2.88 kg vs 1.29 kg, = .003), and achieved greater reduction in hemoglobin A (-0.87% vs +0.87%, = .001) and systolic blood pressure (-4.35 mmHg vs +0.52 mmHg, = .044). The shared medical appointment model can be effective in treating prediabetes.
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http://dx.doi.org/10.1370/afm.2647DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118487PMC
November 2019

Do patients who have newly identified prediabetes lose weight in the following year?

Fam Pract 2021 Jun 11. Epub 2021 Jun 11.

Cleveland Clinic Community Care, Center for Value Based Care, Cleveland Clinic, Cleveland, OH, USA.

Background: Identifying a window of opportunity when patients are motivated to lose weight might improve the effectiveness of weight loss counseling. The onset of chronic disease could create such a window.

Objective: To determine whether identifying prediabetes was associated with subsequent weight loss.

Methods: Our retrospective cohort study included adults with obesity and a primary care visit between 2015 and 2017. Data were collected and analysed in 2019/2020. We compared patients who developed prediabetes [haemoglobin A1c (HbA1c) ≥5.7 and <6.5] to patients with a normal HbA1c (<5.7). We ran linear regression models to identify the association between identifying prediabetes and percent body mass index (BMI) change at 6 and 12 months. The adjusted model controlled for demographic characteristics at baseline, Charlson comorbidity score, and metformin, antipsychotic, antidepressant and antiobesity medication prescribed in either the first 3 months (for the 6-month outcome) or first 9 months (for 12-month outcome) and clustering within physician.

Results: Of 11 290 participants, 43% developed prediabetes. At 6 months, 15% of the prediabetes group lost ≥5% of their BMI compared with 13% of the comparison group. The results were similar at 12 months with 18% of the prediabetes group losing ≥5% of their BMI compared with 17%. The prediabetes group lost a higher percentage of their BMI (β = -0.7% versus -0.3% at 6 months and β = -0.5% versus 0.01% at 12 months).

Conclusions: While the percent of BMI change was small, patients with newly identified prediabetes lost more weight than a comparison group.
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http://dx.doi.org/10.1093/fampra/cmab049DOI Listing
June 2021

Oral Temperature of Noninfected Hospitalized Patients.

JAMA 2021 05;325(18):1899-1901

Center for Value-Based Care Research, Cleveland Clinic, Cleveland, Ohio.

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http://dx.doi.org/10.1001/jama.2021.1541DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114137PMC
May 2021

Comparison of National Data Sources to Assess Preventive Care in the US Population.

J Gen Intern Med 2021 Mar 31. Epub 2021 Mar 31.

Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland Clinic, Cleveland, OH, USA.

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http://dx.doi.org/10.1007/s11606-021-06707-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012018PMC
March 2021

Factors Impacting Physician Referral To and Patient Attendance at Weight Management Programs Within a Large Integrated Health System.

J Gen Intern Med 2021 08 22;36(8):2339-2345. Epub 2021 Jan 22.

Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland, OH, USA.

Background: Identifying which patients receive referrals to and which ones attend weight management programs can provide insights into how physicians manage obesity.

Objective: To describe patient factors associated with referrals, which primarily reflect physician priorities, and attendance, which reflects patient priorities. We also examine the influence of the individual physician by comparing adjusted rates of referral and attendance across physicians.

Design: Retrospective cohort study.

Participants: Adults with a body mass index (BMI) ≥ 30 kg/m who had a primary care visit between 2015 and 2018 at a large integrated health system MAIN MEASURES: Referrals and visits to programs were collected from the EHR in 2019 and analyzed in 2019-2020. Multilevel logistic regression models were used to identify the association between patient characteristics and (1) receiving a referral, and (2) attending a visit after a referral. We compared physicians' adjusted probabilities of referring patients and of their patients attending a visit.

Key Results: Our study included 160,163 adults, with a median BMI of 35 kg/m. Seventeen percent of patients received ≥ 1 referral and 29% of those attended a visit. The adjusted odds of referral increased 57% for patients with a BMI 35-39 (versus 30-34) and 32% for each comorbidity (p < 0.01). Attending a visit was less strongly associated with BMI (aOR 1.18 for 35-39 versus 30-34, 95% CI 1.09-1.27) and not at all with comorbidity. For the physician-level analysis, the adjusted probability of referral had a much wider range (0 to 83%; mean = 19%) than did the adjusted probability of attendance (range 27 to 34%).

Conclusions: Few patients attended a weight management program. Physicians vary greatly in their probability of referring patients to programs but not in their patients' probability of attending.
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http://dx.doi.org/10.1007/s11606-020-06520-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342643PMC
August 2021

Patients Desire Personalized, Specific, and Continuous Advice on Weight Management.

South Med J 2021 01;114(1):41-45

From the Center for Value-Based Care Research and the Department of Family Medicine, Cleveland Clinic Community Care, the Strategy Office, Cleveland Clinic, and the Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio.

Objective: To deliver effective care, healthcare systems should understand patients' preferences for weight management across a spectrum of needs. Our objective was to describe patients' perceptions of what helps or hinders weight loss and maintenance.

Methods: Semistructured interviews were conducted with patients who accessed weight management services at a large integrated health system in 2018. The interview guide was developed and iteratively refined through a literature search and by consulting experts. Questions included the respondent's weight history, interactions with the health system, and current health status. The analysis used a grounded theory approach, and each transcript was double-coded in 2019. Codes were sorted into themes. All discrepancies were resolved through team discussion.

Results: Fifteen patients were interviewed. The majority of respondents (87%) reported multiple weight loss attempts. Three themes were identified. First, advice should be matched to a patient's knowledge and prior experience (eg, using bariatric deck cards). As patients progressed, clinician advice also needed to advance (eg, explaining how to expand food options instead of defining a healthy diet). Second, respondents had a variety of motivating factors, and understanding where motivation is generated from can inform how to design a weight management approach. Third, patients need continual and long-term advice. Some respondents feared becoming ineligible for services if their weight dropped too much.

Conclusions: Health systems can support patients by developing processes for identifying the extent of a patient's knowledge and giving personalized advice based on the patient's preferences and experiences. Reassessing needs at defined intervals may help patients attain and sustain their goals.
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http://dx.doi.org/10.14423/SMJ.0000000000001196DOI Listing
January 2021

Association Between Pain, Blood Pressure, and Medication Intensification in Primary Care: an Observational Study.

J Gen Intern Med 2020 12 21;35(12):3549-3555. Epub 2020 Sep 21.

Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland Clinic, 9500 Euclid Avenue, G10, Cleveland, OH, 44195, USA.

Background: Treating hypertension is important but physicians often do not intensify blood pressure (BP) treatment in the setting of pain.

Objective: To identify whether reporting pain is associated with (1) elevated BP at the same visit, (2) medication intensification, and (3) elevated BP at the subsequent visit.

Design: Retrospective cohort SETTING: Integrated health system PARTICIPANTS: Adults seen in primary care EXPOSURE: Pain status based on numerical scale: mild (1-3), moderate (4-6), or severe (≥ 7).

Main Measures: We defined elevated BP as ≥ 140/80 mmHg and medication intensification as increasing the dose or adding a new antihypertensive medication. Multilevel regression models were used to find the association between pain and (1) elevated BP at the index visit; (2) medication intensification at the index visit; and (3) elevated BP at the subsequent visit. Models adjusted for demographics, chronic conditions, and clustering within physician. In the third model, we adjusted for initial systolic BP as well.

Key Results: Our population included 56,322 patients; 3155 (6%) reported mild pain, 5050 (9%) reported moderate pain, and 4647 (8%) reported severe pain at the index visit. Compared with no pain, the adjusted odds ratios of elevated BP were 1.38 (95% CI: 1.28-1.48) for severe pain, 1.06 (95% CI: 0.99-1.14) for moderate pain, and 1.02 (95% CI: 0.93-1.12) for mild pain. Adjusted odds ratios of medication intensification at the index visit were 0.65 (95% CI: 0.54-0.80) for mild pain, 0.61 (95% CI: 0.52-0.72) for moderate pain, and 0.55 (95% CI: 0.47-0.64) for severe pain. Among patients with elevated BP at the index visit, reporting pain at the index visit was not associated with elevated BP at the subsequent visit.

Conclusions: When patients reported pain, physicians were less likely to intensify antihypertensive treatment; nevertheless, patients reporting pain were not more likely to have elevated BP at the subsequent visit.
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http://dx.doi.org/10.1007/s11606-020-06208-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728880PMC
December 2020

Late Diagnosis of COVID-19 in Patients Admitted to the Hospital.

J Gen Intern Med 2020 09 15;35(9):2829-2831. Epub 2020 Jun 15.

Center for Value-Based Care Research, Cleveland Clinic Community Care, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.

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http://dx.doi.org/10.1007/s11606-020-05949-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295323PMC
September 2020

Association between shared medical appointments and weight loss outcomes and anti-obesity medication use in patients with obesity.

Obes Sci Pract 2020 Jun 25;6(3):247-254. Epub 2020 Feb 25.

Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio.

Objective: In shared medical appointments (SMAs), multiple patients with a similar clinical diagnosis are seen by a multidisciplinary team for interactive group sessions. Very few studies have specifically studied SMAs and weight loss in patients with obesity. This study compared weight loss outcomes and anti-obesity medication (AOM) access between patients with obesity managed through (SMAs) versus individual appointments.

Methods: Retrospective study of adults seen for obesity between September 2014 and February 2017 at Cleveland Clinic Institute of Endocrinology and Metabolism. Percent weight loss from baseline was compared between two propensity score-matched populations: patients who attended ≥1 SMA and patients managed with individual medical appointments.

Results: From all eligible patients identified (n=310 SMA, n=1,993 non-SMA), 301 matched pairs were evaluated for weight loss. The SMA group (n=301) lost a mean of 4.2%, 5.2% and 3.8% of baseline weight over 6, 12 and 24 months; the non-SMA group (n=301) lost significantly less weight (1.5%, 1.8% and 1.6%, respectively) (paired -test, <.05). All patients were eligible for US Food and Drug Administration-approved AOMs based on obesity diagnosis; however, 49.8% (150/301) of matched SMA patients were prescribed an AOM versus 12.3% (37/301) of matched non-SMA patients.

Conclusion: This study suggests that SMAs may offer a promising alterative for obesity management and one that may facilitate greater utilization of AOMs. In propensity score-matched cohorts, SMAs were associated with greater weight loss outcomes when compared to usual care facilitated through individual medical appointments alone.
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http://dx.doi.org/10.1002/osp4.406DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278906PMC
June 2020

The Impact of Systematic Depression Screening in Primary Care on Depression Identification and Treatment in a Large Health Care System: A Cohort Study.

J Gen Intern Med 2020 11 3;35(11):3141-3147. Epub 2020 Jun 3.

Center for Value-Based Care Research, Cleveland Clinic, Cleveland, OH, USA.

Background: Unless implementation of systematic depression screening is associated with timely treatment, quality measures based on screening are unlikely to improve outcomes.

Objective: To assess the impact of integrating systematic depression screening with clinical decision support on depression identification and treatment.

Design: Retrospective pre-post study.

Participants: Adults with a primary care visit within a large integrated health system in 2016 were included. Adults diagnosed with depression in 2015 or prior to their initial primary care visit in 2016 were excluded.

Intervention: Initiation of systematic screening using the Patient Health Questionnaire (PHQ) which began in mid-2016.

Main Measures: Depression diagnosis was based on ICD codes. Treatment was defined as (1) antidepressant prescription, (2) referral, or (3) evaluation by a behavioral health specialist. We used an adjusted linear regression model to identify whether the percentage of visits with a depression diagnosis was different before versus after implementation of systematic screening. An adjusted multilevel regression model was used to evaluate the association between screening and odds of treatment.

Key Results: Our study population included 259,411 patients. After implementation, 59% of patients underwent screening. Three percent scored as having moderate to severe depression. The rate of depression diagnosis increased by 1.2% immediately after systematic screening (from 1.7 to 2.9%). The percent of patients with diagnosed depression who received treatment within 90 days increased from 64% before to 69% after implementation (p < 0.01) and the adjusted odds of treatment increased by 20% after implementation (AOR 1.20, 95% CI 1.12-1.28, p < 0.01).

Conclusions: Implementing systematic depression screening within a large health care system led to high rates of screening and increased rates of depression diagnosis and treatment.
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http://dx.doi.org/10.1007/s11606-020-05856-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661597PMC
November 2020

The Six-Clicks Mobility Measure: A Useful Tool for Predicting Discharge Disposition.

Arch Phys Med Rehabil 2020 07 6;101(7):1199-1203. Epub 2020 Apr 6.

Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, Cleveland, Ohio.

Objective: To assess the predictive capabilities of 2 measures of functional mobility, the 6-clicks score and the Braden scale mobility score. We also identified the additional predictive value of adding electronic health record data (demographics, laboratory data, and vital signs) to each model.

Design: Cohort study.

Setting: A large integrated health system.

Participants: Patients ≥18 years of age (N=17,022) admitted to the inpatient medical service of one of 8 hospitals.

Interventions: None.

Main Outcome Measures: Predictive measures were patient demographics, laboratory values, vital signs, and functional mobility as measured by the 6-clicks score within the first 48 hours of hospital admission. Our outcome was discharge destination (home vs other).

Results: Our final sample included 19,963 records. Patients were discharged alive from 19,698 admissions. The majority were women (n=11,729, 59%) with a mean age of 73 (standard deviation, 15.3) years. Patients' initial 6-clicks score had moderate discrimination for discharge destination (c-statistic of 0.78) and outperformed the Braden score (c-statistic of 0.68). Electronic health record data alone had poor discrimination (c-statistic of 0.66) and added little to the model of 6-clicks alone (adjusted c-statistic increased from 0.78 to 0.80).

Conclusion: Functional mobility measured via 6-clicks within 48 hours of admission can help identify patients who are likely to go home, facilitating early discharge planning.
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http://dx.doi.org/10.1016/j.apmr.2020.02.016DOI Listing
July 2020

The Effect of Starting the Protein-Sparing Modified Fast on Weight Change over 5 years.

J Gen Intern Med 2020 03 8;35(3):704-710. Epub 2020 Jan 8.

Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland Clinic, Cleveland, OH, 44195, USA.

Background: Ketogenic diets have been highlighted as a way to lose weight while experiencing reduced hunger. The protein-sparing modified fast (PSMF) induces ketosis but may be difficult to maintain.

Objective: To track weight loss for individuals initiating PSMF versus all other diets (e.g., balanced, high protein) for up to 5 years.

Design: Retrospective cohort study PARTICIPANTS: Adults who discussed the PSMF with a clinician between 2007 and 2014 INTERVENTION: Initiating the PSMF diet versus other diets MEASURES: The main outcome was percent weight change up to 5 years. Demographic and health data were collected using electronic health records. We fit regression models including age, sex, race, insurance, new medication prescriptions, and specialist visit to identify the effect of PSMF diet on percent weight change. We grouped patients by percent weight change at each year (≥ 5% loss, 4% loss to 4% gain, ≥ 5% gain) and used Pearson χ tests to compare proportions.

Results: Of 1,403 eligible patients, 879 (63%) started the PSMF. The PSMF group was slightly younger (52 vs. 54 years, p < 0.01) and had a higher body mass index (41.9 kg/m vs. 40.4 kg/m, p < 0.001). In the adjusted analysis, the PSMF group averaged 3% more weight loss than the other group over the 5-year follow-up (95% CI - 3.5, - 2.0, p < 0.001). PSMF patients lost more weight initially, but by year 4, there was no difference between diets (1.6% versus 1.3%, PSMF versus other diets, p = 0.12). Patients starting the PSMF were more likely to experience ≥ 5% weight loss at 1 year (55% vs 20%, p < 0.001) and 3 years (33% vs. 23% p < 0.05), but not 5 years (34% vs 29%, p = 0.16, PSMF versus other diets, respectively).

Conclusions: In clinical practice, the PSMF achieves rapid weight loss in the first 6 months, but only a small percentage of patients maintained significant weight loss long term.
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http://dx.doi.org/10.1007/s11606-019-05535-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080885PMC
March 2020

Attitudes of High Versus Low Antibiotic Prescribers in the Management of Upper Respiratory Tract Infections: a Mixed Methods Study.

J Gen Intern Med 2020 04 19;35(4):1182-1188. Epub 2019 Oct 19.

Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, USA.

Importance: Inappropriate antibiotic use for upper respiratory tract infections (URTIs) is an ongoing problem in primary care. There is extreme variation in the prescribing practices of individual physicians, which cannot be explained by clinical factors.

Objective: To identify factors associated with high and low prescriber status for management of URTIs in primary care practice.

Design And Participants: Exploratory sequential mixed-methods design including interviews with primary care physicians in a large health system followed by a survey. Twenty-nine physicians participated in the qualitative interviews. Interviews were followed by a survey in which 109 physicians participated.

Main Measures: Qualitative interviews were used to obtain perspectives of high and low prescribers on factors that influenced their decision making in the management of URTIs. A quantitative survey was created based on qualitative interviews and responses compared to actual prescribing rates. An assessment of self-prescribing pattern relative to their peers was also conducted.

Results: Qualitative interviews identified themes such as clinical factors (patient characteristics, symptom duration, and severity), nonclinical factors (physician-patient relationship, concern for patient satisfaction, preference and expectation, time pressure), desire to follow evidence-based medicine, and concern for adverse effects to influence prescribing. In the survey, reported concern regarding antibiotic side effects and the desire to practice evidence-based medicine were associated with lower prescribing rates whereas reported concern for patient satisfaction and patient demand were associated with high prescribing rates. High prescribers were generally unaware of their high prescribing status.

Conclusions And Relevance: Physicians report that nonclinical factors frequently influence their decision to prescribe antibiotics for URTI. Physician concerns regarding antibiotic side effects and patient satisfaction are important factors in the decision-making process. Changes in the health system addressing both physicians and patients may be necessary to attain desired prescribing levels.
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http://dx.doi.org/10.1007/s11606-019-05433-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7174444PMC
April 2020

Impact of a system-wide quality improvement initiative on blood pressure control: a cohort analysis.

BMJ Qual Saf 2020 03 31;29(3):225-231. Epub 2019 Aug 31.

Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland Clinic, Cleveland, Ohio, USA.

Objective: To assess the impact of a quality improvement programme on blood pressure (BP) control and determine whether medication intensification or repeated measurement improved control.

Design: Retrospective cohort comparing visits in 2015 to visits in 2016 (when the programme started).

Subjects: Adults with ≥1 primary care visit between January and June in 2015 and 2016 and a diagnosis of hypertension in a large integrated health system.

Measures: Elevated BP was defined as a BP ≥140/90 mm Hg. Physician response was defined as: nothing; BP recheck within 30 days; or medication intensification within 30 days. Our outcome was BP control (<140/90 mm Hg) at the last visit of the year. We used a multilevel logistic regression model (adjusted for demographic and clinical variables) to identify the effect of the programme on the odds of BP control.

Results: Our cohort included 111 867 adults. Control increased from 72% in 2015 to 79% in 2016 (p<0.01). The average percentage of visits with elevated blood pressure was 31% in 2015 and 25% in 2016 (p<0.01). During visits with an elevated BP, physicians were more likely to intensify medication in 2016 than in 2015 (43% vs 40%, p<0.01) and slightly more likely to obtain a BP recheck (15% vs 14%, p<0.01). Among patients with ≥1 elevated BP who attained control by the last visit in the year, there was 6% increase from 2015 to 2016 in the percentage of patients who received at least one medication intensification during the year and a 1% increase in BP rechecks. The adjusted odds of the last BP reading being categorised as controlled was 59% higher in 2016 than in 2015 (95% CI 1.54 to 1.64).

Conclusion: A system-wide initiative can improve BP control, primarily through medication intensification.
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http://dx.doi.org/10.1136/bmjqs-2018-009032DOI Listing
March 2020

Derivation and Validation of a Model to Predict 30-Day Readmission in Surgical Patients Discharged to Skilled Nursing Facility.

J Am Med Dir Assoc 2019 09 5;20(9):1086-1090.e2. Epub 2019 Jun 5.

Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, Cleveland, OH.

Objectives: To identify factors associated with 30-day all-cause readmission rates in surgical patients discharged to skilled nursing facilities (SNFs), and derive and validate a risk score.

Design: Retrospective cohort.

Setting And Participants: Patients admitted to 1 tertiary hospital's surgical services between January 1, 2011, and December 31, 2014 and subsequently discharged to 110 SNFs within a 25-mile radius of the hospital. The first 2 years were used for the derivation set and the last 2 for validation.

Methods: Data were collected on 30-day all cause readmissions, patient demographics, procedure and surgical service, comorbidities, laboratory tests, and prior health care utilization. Multivariate regression was used to identify risk factors for readmission.

Results: During the study period, 2405 surgical patients were discharged to 110 SNFs, and 519 (21.6%) of these patients experienced readmission within 30 days. In a multivariable regression model, hospital length of stay [odds ratio (OR) per day: 1.03, 95% confidence interval (CI) 1.02-1.04], number of hospitalizations in past year (OR 1.24 per hospitalization, 95% CI 1.18-1.31), nonelective surgery (OR 1.33, 95% CI 1.18-1.65), low-risk service (orthopedic/spine service) (OR 0.32, 95% CI 0.25-0.42), and intermediate-risk service (cardiothoracic surgery/urology/gynecology/ear, nose, throat) (OR 0.69, 95% CI 0.53-0.88) were associated with all-cause readmissions. The model had a C index of 0.71 in the validation set. Using the following risk score [0.8 × (hospital length of stay) + 7 × (number of hospitalizations in past year) +10 for nonelective surgery, +36 for high-risk surgery, and +20 for intermediate-risk surgery], a score of >40 identified patients at high risk of 30-day readmission (35.8% vs 12.6%, P < .001).

Conclusions/implications: Among surgical patients discharged to an SNF, a simple risk score with 4 parameters can accurately predict the risk of 30-day readmission.
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http://dx.doi.org/10.1016/j.jamda.2019.04.016DOI Listing
September 2019

Hospital Readmission and Subsequent Decline in Long-Term Survivors of Acute Respiratory Distress Syndrome.

Am J Crit Care 2019 01;28(1):76-80

Amy W. Wozniak is a research associate, Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, and the Outcomes After Critical Illness and Surgery Group, Johns Hopkins University School of Medicine, Baltimore, Maryland. Elizabeth R. Pfoh is associate staff at the Center for Value-Based Care, Medicine Institute, Cleveland Clinic Foundation, Cleveland, Ohio. Victor D. Dinglas is a research associate, Outcomes After Critical Illness and Surgery Group, and Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine. Peter J. Pronovost is a professor, Outcomes After Critical Illness and Surgery Group, and Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine. Dale M. Needham is a professor, Outcomes After Critical Illness and Surgery Group, Division of Pulmonary and Critical Care Medicine, and Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine. Elizabeth Colantuoni is an associate scientist, Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, and Outcomes After Critical Illness and Surgery Group, Johns Hopkins University School of Medicine.

Acute respiratory distress syndrome is associated with long-term physical impairments. Although readmission is common, little is known about the impact of readmissions on the physical status of this population. The purpose of this study was to evaluate the association between hospital readmission, with or without an intensive care unit stay, and physical status in survivors of acute respiratory distress syndrome. The exposure was hospital readmission, categorized as (1) no readmission, (2) readmitted 1 or more times without an intensive care unit stay, or (3) readmitted 1 or more times with an intensive care unit stay. The incidence of readmission was assessed during years 3, 4, and 5 of the study. The outcome was physical decline or death. Decline was evaluated via 3 separate measures: muscle strength, exercise capacity, and self-reported physical function. Of the 132 survivors, 64% (n = 84) had 1 or more readmissions and 27% (n = 35) of them had 1 or more intensive care unit stays. Rates of decline in the year prior were similar regardless of readmission status in the current year. Multivariable logistic regression models indicated that readmission without an intensive care unit stay versus no readmission was not significantly associated with decline. Readmission with an intensive care unit stay versus no readmission was associated with physical decline. Clinicians and researchers should consider the effect of a readmission to an intensive care unit, distinct from hospital readmission, on acute respiratory distress syndrome survivors' physical status.
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http://dx.doi.org/10.4037/ajcc2019580DOI Listing
January 2019

Physicians' Views of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes Not on Insulin.

Ann Fam Med 2018 07;16(4):349-352

Center for Value-Based Care Research, Cleveland Clinic, Cleveland, Ohio.

This qualitative study examines to what extent and why physicans still prescribe self-monitoring of blood glucose (SMBG) in patients with non-insulin-treated type 2 diabetes (NITT2D) when the evidence shows it increases cost without improving hemoglobin A (HbA), general well being, or health-related quality of life. Semistructured phone interviews with 17 primary care physicians indicated that the majority continue to recommend routine self-monitoring of blood glucose due to a compelling belief in its ability to promote the lifestyle changes needed for glycemic control. Targeting physician beliefs about the effectiveness of self-monitoring of blood glucose, and designing robust interventions accordingly, may help reduce this practice.
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http://dx.doi.org/10.1370/afm.2244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6037524PMC
July 2018

Use of PROs in Primary Care: PROMIS or Disappointment?

J Gen Intern Med 2018 08;33(8):1207-1208

Center for Value-Based Care Research, Cleveland Clinic, 9500 Euclid Avenue, G10, Cleveland, OH, 44195, USA.

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http://dx.doi.org/10.1007/s11606-018-4511-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6082225PMC
August 2018

Association Between Number of Preventive Care Guidelines and Preventive Care Utilization by Patients.

Am J Prev Med 2018 07 14;55(1):1-10. Epub 2018 May 14.

Medicine Institute, Cleveland Clinic, Cleveland, Ohio.

Introduction: The number of preventive care guidelines is rapidly increasing. It is unknown whether the number of guideline-recommended preventive services is associated with utilization.

Methods: The authors used Poisson regression of 390,778 person-years of electronic medical records data from 2008 to 2015, in 80,773 individuals aged 50-75 years. Analyses considered eligibility for 11 preventive services most closely associated with guidelines: tobacco cessation; control of obesity, hypertension, lipids, or blood glucose; influenza vaccination; and screening for breast, cervical, or colorectal cancers, abdominal aortic aneurysm, or osteoporosis. The outcome was the rate of preventive care utilization over the following year. Results were adjusted for demographics and stratified by the number of disease risk factors (smoking, obesity, hypertension, hyperlipidemia, diabetes). Data were collected in 2016 and analyzed in 2017.

Results: Preventive care utilization was lower when the number of guideline-recommended preventive services was higher. The adjusted rate of preventive care utilization decreased from 38.67 per 100 (95% CI=38.16, 39.18) in patients eligible for one guideline-recommended service to 31.59 per 100 (95% CI=31.29, 31.89) in patients eligible for two services and 25.43 per 100 (95% CI=24.68, 26.18) in patients eligible for six or more services (p-trend<0.001). Results were robust to disease risk factors and observed for all but two services (tobacco cessation, obesity reduction). However, for any given number of guideline-recommended services, patients with more disease risk factors had higher utilization rates.

Conclusions: The rate of preventive care utilization was lower when the number of guideline-recommended services was higher. Prioritizing recommendations might improve utilization of high-value services.
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http://dx.doi.org/10.1016/j.amepre.2018.03.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6014877PMC
July 2018

Informing the Design of a New Pragmatic Registry to Stimulate Near Miss Reporting in Ambulatory Care.

J Patient Saf 2021 04;17(3):e121-e127

Hofstra Northwell School of Medicine at Lenox Hill Hospital, New York, New York.

Objective: Ambulatory care safety is of emerging concern, especially in light of recent studies related to diagnostic errors and health information technology-related safety. Safety reporting systems in outpatient care must address the top safety concerns and be practical and simple to use. A registry that can identify common near misses in ambulatory care can be useful to facilitate safety improvements. We reviewed the literature on medical errors in the ambulatory setting to inform the design of a registry for collecting near miss incidents.

Methods: This narrative review included articles from PubMed that were: 1) original research; 2) discussed near misses or adverse events in the ambulatory setting; 3) relevant to US health care; and 4) published between 2002 and 2013. After full text review, 38 studies were searched for information on near misses and associated factors. Additionally, we used expert opinion and current inpatient near miss registries to inform registry development.

Results: Studies included a variety of safety issues including diagnostic errors, treatment or management-related errors, communication errors, environmental/structural hazards, and health information technology (health IT)-related concerns. The registry, based on the results of the review, updates previous work by including specific sections for errors associated with diagnosis, communication, and environment structure and incorporates specific questions about the role of health information technology.

Conclusions: Through use of this registry or future registries that incorporate newly identified categories, near misses in the ambulatory setting can be accurately captured, and that information can be used to improve patient safety.
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http://dx.doi.org/10.1097/PTS.0000000000000317DOI Listing
April 2021

How Physician Perspectives on E-Prescribing Evolve over Time. A Case Study Following the Transition between EHRs in an Outpatient Clinic.

Appl Clin Inform 2016 10 26;7(4):994-1006. Epub 2016 Oct 26.

Erika Abramson, MD, MS, Departments of Pediatrics and Healthcare Policy and Research, Weill Cornell Medical College, 525 East 68th Street, Rm M 610A, New York, NY 10065, Tel: 212-746-3929, Fax: 212-746-3140, Email:

Background: Physicians are expending tremendous resources transitioning to new electronic health records (EHRs), with electronic prescribing as a key functionality of most systems. Physician dissatisfaction post-transition can be quite marked, especially initially. However, little is known about how physicians' experiences using new EHRs for e-prescribing evolve over time. We previously published a qualitative case study about the early physician experience transitioning from an older to a newer, more robust EHR, in the outpatient setting, focusing on their perceptions of the electronic prescribing functionality.

Objective: Our current objective was to examine how perceptions about using the new HER evolved over time, again with a focus on electronic prescribing.

Methods: We interviewed thirteen internists at an academic medical center-affiliated ambulatory care clinic who transitioned to the new EHR two years prior. We used a grounded theory approach to analyze semi-structured interviews and generate key themes.

Results: We identified five themes: efficiency and usability, effects on safety, ongoing training requirements, customization, and competing priorities for the EHR. We found that for even experienced e-prescribers, achieving prior levels of perceived prescribing efficiency took nearly two years. Despite the fact that speed in performing prescribing-related tasks was highly important, most were still not utilizing system short cuts or customization features designed to maximize efficiency. Alert fatigue remained common. However, direct transmission of prescriptions to pharmacies was highly valued and its benefits generally outweighed the other features considered poorly designed for physician workflow.

Conclusions: Ensuring that physicians are able to do key prescribing tasks efficiently is critical to the perceived value of e-prescribing applications. However, successful transitions may take longer than expected and e-prescribing system features that do not support workflow or require constant upgrades may further prolong the process. Additionally, as system features continually evolve, physicians may need ongoing training and support to maintain efficiency.
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http://dx.doi.org/10.4338/ACI-2016-04-RA-0069DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5228140PMC
October 2016

A qualitative exploration of favorite patients in primary care.

Patient Educ Couns 2016 11 21;99(11):1888-1893. Epub 2016 Jun 21.

Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.

Objective: To ascertain whether physicians have favorite patients, their experiences with such patients, and how such relationships may influence patients and physicians.

Methods: Semi-structured key informant interviews with 25 primary care internists practicing in several clinic settings at a large academic medical center.

Results: The term 'favorite patient' raised concerns regarding boundaries and favoritism. Nevertheless, most participants (22/25) reported having favorite patients. For many physicians, favorite patients were not necessarily the most compliant patients, or those most similar to them. Instead, favorite patients were often very sick patients and/or those who have known their physicians for a long time. Many of these relationships were defined by experiences that strengthened the patient-physician bond. Participants felt that the favorite patient bond had a positive effect on patients and physicians ("it improves my day"). Physicians also discussed their challenging patients unprompted. Participants voiced that being cognizant of having favorite and challenging patients help to prevent favoring the care of certain patients over others.

Conclusions & Practice Implications: Primary care physicians value patient relationships and benefit from deep bonds. A better understanding of how favorite patients affect primary care physicians could help inform and improve relationships with all patients.
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http://dx.doi.org/10.1016/j.pec.2016.06.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763489PMC
November 2016

Physical declines occurring after hospital discharge in ARDS survivors: a 5-year longitudinal study.

Intensive Care Med 2016 Oct 16;42(10):1557-1566. Epub 2016 Sep 16.

Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Purpose: Survivors of acute respiratory distress syndrome (ARDS) are at high risk for new or ongoing physical declines after hospital discharge. The objective of our study was to evaluate the epidemiology of physical declines over 5-year follow-up and identify patients at risk for decline.

Methods: This multi-site prospective cohort study evaluated ARDS survivors who completed a physical status assessment at 3 or 6 months post-discharge. Three measures were evaluated: muscle strength (Medical Resource Council sumscore); exercise capacity [6-min walk test (6MWT)]; physical functioning [36-Item Short Form Health Survey (SF-36 survey)]. Patients were defined as "declined" if a comparison of their current and prior score showed a decrease that was greater than the Reliable Change Index-or if the patient died. Risk factors [pre-ARDS baseline status, intensive care unit (ICU) illness severity, and other intensive care variables] were evaluated using longitudinal, generalized linear regression models for each measure.

Results: During the follow-up of 193 ARDS survivors (55 % male; median age 49 years), 166 (86 %) experienced decline in ≥1 physical measure (including death) and 133 (69 %) experienced a physical decline (excluding death). For all measures, age was a significant risk factor [odds ratios (OR) 1.34-1.69 per decade; p < 0.001]. Pre-ARDS comorbidity (Charlson Index) was independently associated with declines in strength and exercise capacity (OR 1.10 and 1.18, respectively; p < 0.02), and organ failure [maximum daily Sequential Organ Failure Assessment (SOFA) score in ICU] was associated with declines in strength (OR 1.06 per 1 point of SOFA score; p = 0.02).

Conclusions: Over the follow-up period, the majority of ARDS survivors experienced a physical decline, with older age and pre-ICU comorbidity being important risk factors for this decline.
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http://dx.doi.org/10.1007/s00134-016-4530-1DOI Listing
October 2016

Understanding age and race disparities in the application of sentinel lymph node biopsy in breast cancer.

J Investig Med 2016 12 27;64(8):1241-1245. Epub 2016 Jul 27.

Department of Family Medicine, University Hospitals Case Medical Center, Cleveland, Ohio, USA.

Sentinel lymph node biopsy (SLNB) is the standard of care for surgical evaluation of early-stage breast cancer and is being employed as a quality metric for accreditation of breast centers. Previous studies report disparities in SLNB receipt. The goal of this study is to determine SLNB rates and explore rationale for non-receipt of SLNB. Patients with early-stage breast cancer diagnosed between 2010 and 2011 were identified from the University Hospitals Case Medical Center tumor registry. Multivariable logistic models were used to identify clinical and demographic risk factors for patients who did not receive SLNB. We performed chart reviews to elucidate reasons for the lack of SLNB. Our total sample was 479 patients; of them 432 (90.2%) received SLNB. On average, patients who received SLNB were younger than those who did not receive SLNB (61 compared to 79 years, respectively). Patients ≥80 years were 96% less likely to receive SLNB compared to patients <65 years (OR 0.04; 95% CI 0.00 to 0.14). There were no differences in SLNB by race, between patients undergoing Medicare or Medicaid and managed care, by surgeon specialty, or across medical centers. Chart review determined that 45/47 patients did not have SLNB, because it was a clinical decision-making; advanced age (>80 years) was cited in 27/47 women. Older women had much lower odds of receiving SLNB; however, non-receipt of SLNB was often due to a clinical reasoning. Our study highlights the importance of clinical reasoning in receiving SLNB, whereas other studies solely employing administrative databases do not.
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http://dx.doi.org/10.1136/jim-2016-000226DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5561424PMC
December 2016

Use of Newly Covered Versus Established Preventive Care Screening: Comparison of Depression and Smoking Screening.

J Healthc Qual 2017 Nov/Dec;39(6):e91-e101

Pay-for-value initiatives include both depression and smoking screening. Evaluating how patterns of care differ for an established screening (smoking) versus newer screening (depression) can help programs better implement these measures. Our objective is to evaluate (1) patterns of smoking and depression screening and (2) how patient factors affect screening patterns. We analyzed retrospectively collected electronic health record data from 4,763 Medicare-patients in 34 primary care practices between 2010 and 2012. The relationship between multimorbidity, history of stroke, and having depression on receipt of screening was evaluated. The outcome variables were no screening, smoking screening only, or concurrent smoking and depression screening. Fifty percent of patients were screened for smoking at every visit and never screened for depression (n = 2,378). Twelve percent of patients with ≥five visits received both depression and smoking-status screens on each of their first five visits. Screening patterns varied significantly across sites. For example, one site screened approximately 87% of patients for both depression and smoking-status at every visit. Another site screened 93% of patients for smoking during the first visit but did not conduct depression screening. Programs considering initiating new screenings should evaluate the clinic-specific workflow of successful screenings and integrate new screenings using the same strategy.
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http://dx.doi.org/10.1097/JHQ.0000000000000037DOI Listing
July 2018

The SF-36 Offers a Strong Measure of Mental Health Symptoms in Survivors of Acute Respiratory Failure. A Tri-National Analysis.

Ann Am Thorac Soc 2016 08;13(8):1343-50

3 Outcomes after Critical Illness and Surgery Group.

Rationale: Survivors of acute respiratory failure commonly experience long-term psychological sequelae and impaired quality of life. For researchers interested in general mental health, using multiple condition-specific instruments may be unnecessary and inefficient when using the Medical Outcomes Study Short Form (SF)-36, a recommended outcome measure, may suffice. However, relationships between the SF-36 scores and commonly used measures of psychological symptoms in acute survivors of respiratory failure are unknown.

Objectives: Our objective is to examine the relationship of the SF-36 mental health domain (MH) and mental health component summary (MCS) scores with symptoms of depression, anxiety, and post-traumatic stress disorder (PTSD) evaluated using validated psychological instruments.

Methods: We conducted a cross-sectional analysis of 1,229 participants at 6- and 12-month follow-up assessment using data from five studies from the United States, the United Kingdom, and Australia.

Measurements And Main Results: Symptoms were assessed using the Hospital Anxiety and Depression Scale (HADS), Depression Anxiety Stress Scales, the Davidson Trauma Scale, Impact of Event Scale (IES), and IES-Revised (IES-R). At 6-month assessment there were moderate to strong correlations of the SF-36 MH scores with HADS depression and anxiety symptoms (r = -0.74 and -0.79) and with IES-R PTSD symptoms (r = -0.60) in the pooled analyses. Using the normalized population mean of 50 on the SF-36 MH domain score as a cut-off, positive predictive values were 16 and 55% for substantial depression; 20 and 68% for substantial anxiety (Depression Anxiety Stress Scales and HADS, respectively); and 40, 44, and 67% for substantial PTSD symptoms (IES-R, IES, and Davidson Trauma Scale, respectively). Negative predictive values were high. The area under the receiver operating characteristics curve of the SF-36 MH score was high for depression, anxiety, and PTSD symptoms (0.88, 0.91, and 0.84, respectively). All results were consistent for the MCS, across the individual studies, and for the 12-month assessment.

Conclusions: For researchers interested in general mental health status, the SF-36 MH or MCS offers a strong measure of psychological symptoms prevalent among survivors of acute respiratory failure. For researchers interested in specific conditions, validated psychological instruments should be considered.
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http://dx.doi.org/10.1513/AnnalsATS.201510-705OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021072PMC
August 2016

Capsule Commentary on Weng et al., Assessing the Quality of Osteoporosis Care in Practice.

Authors:
Elizabeth R Pfoh

J Gen Intern Med 2015 Nov;30(11):1702

Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA.

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http://dx.doi.org/10.1007/s11606-015-3376-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617950PMC
November 2015

Conformance to Depression Process Measures of Medicare Part B Beneficiaries in Primary Care Settings.

J Am Geriatr Soc 2015 Jul 26;63(7):1338-45. Epub 2015 Jun 26.

Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.

Objectives: To evaluate conformance to depression screening, management, and outcome quality indicators and to evaluate individual characteristics associated with conformance to these indicators.

Design: Cross-sectional study using electronic health record (EHR) data.

Setting: Thirty-four clinics in one healthcare system.

Participants: Medicare beneficiaries aged 65 and older with at least one primary care visit between September 2010 and August 2012 (N = 5,000).

Measurements: Seven measures, current as of 2013, were found for which all the necessary specifications were available in the EHR: general screening, screening within 4 months of diagnosis, screening after stroke, screening after heart disease, depression reassessment, depression response, and depression remission. Multilevel logistic regression analyses were used to determine factors associated with conformance.

Results: Screening for depression in Medicare beneficiaries was low (17%). Conformance to measures varied from 10% for the depression response measure to 77% for the depression remission measure. In the adjusted regression analyses for the general screening (adjusted odds ratio (AOR) = 1.45, 95% confidence interval (CI) = 1.01-2.08), depression reassessment (AOR = 4.19, 95% CI = 1.16-15.19), and screening after heart disease (AOR = 5.57, 95% CI = 1.37-22.57) measures, black participants were more likely to be given care that conformed to the numerator criteria than white participants. A strong site effect was found, with 90% of the depression screens being administered at three sites.

Conclusion: Only a small proportion of Medicare beneficiaries received the recommended screening and follow-up care needed to conform to the quality measures for depression in the primary care setting. Further evaluation of measures of depression care should be conducted before these measures are implemented widely.
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http://dx.doi.org/10.1111/jgs.13483DOI Listing
July 2015
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