Linking Primary Care Patients to Mental Health Care via Behavioral Health Social Workers: A Stepped-Wedge Study.

OBJECTIVE: Demand for systematic linkage of patients to behavioral health care has increased because of the widespread implementation of depression screening. This study assessed the impact of deploying behavioral health social workers (BHSWs) in primary care on behavioral health visits for depression or anxiety. METHODS: This quasi-experimental, stepped-wedge study included adults with a primary care visit between 2016 and 2019 at Cleveland Clinic, a large integrated health system. BHSWs were deployed in 40 practices between 2017 and 2019. Patients were allocated to a control group (diagnosed before BHSW deployment) and an intervention group (diagnosed after deployment). Data were collected on behavioral health visits (i.e., to therapists and psychiatrists) within 30 days of the diagnosis. Multilevel logistic regression models identified associations between BHSW deployment period and behavioral health visit, adjusted for demographic variables and clustering within each group. RESULTS: Of 68,659 persons with a diagnosis, 21% had a depression diagnosis, 49% an anxiety diagnosis, and 31% both diagnoses. In the period after BHSW deployment, the proportion of patients with depression who had a behavioral health visit increased by 10 percentage points, of patients with anxiety by 9 percentage points, and of patients with both disorders by 11 percentage points. The adjusted odds of having a behavioral health visit was higher in the postdeployment period for patients with depression (adjusted odds ratio [AOR]=4.35, 95% confidence interval [CI]=3.50-5.41), anxiety (AOR=4.27, 95% CI=3.57-5.11), and both (AOR= 3.26, 95% CI=2.77-3.84). CONCLUSIONS: Integration of BHSWs in primary care was associated with increased behavioral health visits.

Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence.

Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We conducted a retrospective cohort study of hospitalizations at a tertiary academic medical center and its branches from January 2011 to May 2018. A consecutive sample of all hospitalizations in the study period were included. Algorithms were trained on medical, sociodemographic, and institutional variables to predict readmission, length of stay (LOS), and death within 48-72 h. Prediction performance was measured by area under the receiver operator characteristic curve (AUC), Brier score loss (BSL), which measures how well predicted probability matches observed probability, and other metrics. Interpretations were generated using multiple feature extraction algorithms. The study cohort included 1,485,880 hospitalizations for 708,089 unique patients (median age of 59 years, first and third quartiles (QI) [39, 73]; 55.6% female; 71% white). There were 211,022 30-day readmissions for an overall readmission rate of 14% (for patients ≥65 years: 16%). Median LOS, including observation and labor and delivery patients, was 2.94 days (QI [1.67, 5.34]), or, if these patients are excluded, 3.71 days (QI [2.15, 6.51]). Predictive performance was as follows: 30-day readmission (AUC 0.76/BSL 0.11); LOS > 5 days (AUC 0.84/BSL 0.15); death within 48-72 h (AUC 0.91/BSL 0.001). Explanatory diagrams showed factors that impacted each prediction.

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

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.

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients.

In December 2017, the National Academy of Neuropsychology convened an interorganizational Summit on Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients in Denver, Colorado. The Summit brought together representatives of a broad range of stakeholders invested in the care of older adults to focus on the topic of cognitive health and aging. Summit participants specifically examined questions of who should be screened for cognitive impairment and how they should be screened in medical settings. This is important in the context of an acute illness given that the presence of cognitive impairment can have significant implications for care and for the management of concomitant diseases as well as pose a major risk factor for dementia. Participants arrived at general principles to guide future screening approaches in medical populations and identified knowledge gaps to direct future research. Key learning points of the summit included: recognizing the importance of educating patients and healthcare providers about the value of assessing current and baseline cognition;emphasizing that any screening tool must be appropriately normalized and validated in the population in which it is used to obtain accurate information, including considerations of language, cultural factors, and education; andrecognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time.

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients.

In December 2017, the National Academy of Neuropsychology convened an interorganizational Summit on Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients in Denver, Colorado. The Summit brought together representatives of a broad range of stakeholders invested in the care of older adults to focus on the topic of cognitive health and aging. Summit participants specifically examined questions of who should be screened for cognitive impairment and how they should be screened in medical settings. This is important in the context of an acute illness given that the presence of cognitive impairment can have significant implications for care and for the management of concomitant diseases as well as pose a major risk factor for dementia. Participants arrived at general principles to guide future screening approaches in medical populations and identified knowledge gaps to direct future research. Key learning points of the summit included: recognizing the importance of educating patients and healthcare providers about the value of assessing current and baseline cognition; emphasizing that any screening tool must be appropriately normalized and validated in the population in which it is used to obtain accurate information, including considerations of language, cultural factors, and education; and recognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time.

Use of Visual Decision Aids in Physician-Patient Communication: A Pilot Investigation.

INTRODUCTION: A risk calculator paired with a personalized decision aid (RC&DA) may foster shared decision-making in primary care. We assessed the feasibility of using an RC&DA with patients in a primary care outpatient clinic and patients' experiences regarding communication and decision-making. METHODS: This pilot study was conducted with 15 patients of 3 primary care physicians at a clinic within a tertiary medical center. An atherosclerotic cardiovascular disease (ASCVD) risk calculator was used to generate a personalized RC&DA that displayed absolute 10-year risk information as an icon array graphic. Patient perceptions of utility of the RC&DA, preferences for decision-making, and uncertainty with risk reduction decisions were measured with a semi-structured interview. RESULTS: Patients reported that the RC&DA was easy to understand and knowledge gained was useful to modify their ASCVD risk. Patients used the RC&DA to make decisions and reported low uncertainty with those decisions. CONCLUSIONS: Our findings demonstrate the feasibility of, and positive patient experiences related to using, an RC&DA to facilitate shared decision-making between physicians and patients in an outpatient primary care setting.

High-Cost Patients: Hot-Spotters Don't Explain the Half of It.

BACKGROUND: Understanding resource utilization patterns among high-cost patients may inform cost reduction strategies. OBJECTIVE: To identify patterns of high-cost healthcare utilization and associated clinical diagnoses and to quantify the significance of hot-spotters among high-cost users. DESIGN: Retrospective analysis of high-cost patients in 2012 using data from electronic medical records, internal cost accounting, and the Centers for Medicare and Medicaid Services. K-medoids cluster analysis was performed on utilization measures of the highest-cost decile of patients. Clusters were compared using clinical diagnoses. We defined "hot-spotters" as those in the highest-cost decile with ≥4 hospitalizations or ED visits during the study period. PARTICIPANTS AND EXPOSURE: A total of 14,855 Medicare Fee-for-service beneficiaries identified by the Medicare Quality Resource and Use Report as having received 100 % of inpatient care and ≥90 % of primary care services at Cleveland Clinic Health System (CCHS) in Northeast Ohio. The highest-cost decile was selected from this population. MAIN MEASURES: Healthcare utilization and diagnoses. KEY RESULTS: The highest-cost decile of patients (n = 1486) accounted for 60 % of total costs. We identified five patient clusters: "Ambulatory," with 0 admissions; "Surgical," with a median of 2 surgeries; "Critically Ill," with a median of 4 ICU days; "Frequent Care," with a median of 2 admissions, 3 ED visits, and 29 outpatient visits; and "Mixed Utilization," with 1 median admission and 1 ED visit. Cancer diagnoses were prevalent in the Ambulatory group, care complications in the Surgical group, cardiac diseases in the Critically Ill group, and psychiatric disorders in the Frequent Care group. Most hot-spotters (55 %) were in the "frequent care" cluster. Overall, hot-spotters represented 9 % of the high-cost population and accounted for 19 % of their overall costs. CONCLUSIONS: High-cost patients are heterogeneous; most are not so-called "hot-spotters" with frequent admissions. Effective interventions to reduce costs will require a more multi-faceted approach to the high-cost population.

Preventable Admissions on a General Medicine Service: Prevalence, Causes and Comparison with AHRQ Prevention Quality Indicators-A Cross-Sectional Analysis.

BACKGROUND: Rates of preventable admissions will soon be publicly reported and used in calculating performance-based payments. The current method of assessing preventable admissions, the Agency of Healthcare Research and Quality (AHRQ) Preventable Quality Indicators (PQI) rate, is drawn from claims data and was originally designed to assess population-level access to care. OBJECTIVE: To identify the prevalence and causes of preventable admissions by attending physician review and to compare its performance with the PQI tool in identifying preventable admissions. DESIGN: Cross-sectional survey. SETTING: General medicine service at an academic medical center. PARTICIPANTS: Consecutive inpatient admissions from December 1-15, 2013. MAIN MEASURES: Survey of inpatient attending physicians regarding the preventability of the admissions, primary contributing factors and feasibility of prevention. For the same patients, the PQI tool was applied to determine the claims-derived preventable admission rate. KEY RESULTS: Physicians rated all 322 admissions and classified 122 (38 %) as preventable, of which 31 (25 %) were readmissions. Readmissions were more likely to be rated preventable than other admissions (49 % vs. 35 %, p = 0.04). Application of the AHRQ PQI methodology identified 75 (23 %) preventable admissions. Thirty-one admissions (10 %) were classified as preventable by both methods, and the majority of admissions considered preventable by the AHRQ PQI method (44/78) were not considered preventable by physician assessment (K = 0.04). Of the preventable admissions, physicians assigned patient factors in 54 (44 %), clinician factors in 36 (30 %) and system factors in 32 (26 %). CONCLUSIONS: A large proportion of admissions to a general medicine service appeared preventable, but AHRQ's PQI tool was unable to identify these admissions. Before initiation of the PQI rate for use in pay-for-performance programs, further study is warranted.

Quantifying the defensive medicine contribution to primary care costs.

BACKGROUND: Defensive medicine represents one cause of economic losses in healthcare. Studies that measured its cost have produced conflicting results. OBJECTIVE: To directly measure the proportion of primary care costs attributable to defensive medicine. RESEARCH DESIGN AND METHODS: Six-week prospective study of primary care physicians from four outpatient practices. On 3 distinct days, participants were asked to rate each order placed the day before on the extent to which it represented defensive medicine, using a 5-point scale from 0 (not at all defensive) to 4 (entirely defensive). MAIN OUTCOME MEASURES: This study calculated the order defensiveness score for each order (the defensiveness/4) and the physician defensive score (the mean of all orders defensiveness scores). Each order was assigned a weighted cost by multiplying the total cost of that order (based on Medicare reimbursement rates) by the order defensiveness score. The proportion of total cost attributable to defensive medicine was calculated by dividing the weighted cost of defensive orders by the total cost of all orders. RESULTS: Of 50 eligible physicians, 23 agreed to participate; 21 returned the surveys and rated 1234 individual orders on 347 patients. Physicians wrote an average of 3.6 ± 1.0 orders/visit with an associated total cost of $72.60 ± 18.5 per order. Across physicians, the median physician defensive score was 0.018 (IQR = [0.008, 0.049]) and the proportion of costs attributable to defensive medicine was 3.1% (IQR = [0.5%, 7.2%]). Physicians with defensive scores above vs below the median had a similar number of orders and total costs per visit. Physicians were more likely to place defensive orders if trained in community hospitals vs academic centers (OR = 4.29; 95% CI = 1.55-11.86; p = 0.01). CONCLUSIONS: This study describes a new method to directly quantify the cost of defensive medicine. Defensive medicine appears to have minimal impact on primary care costs.

Meaningful measurement: developing a measurement system to improve blood pressure control in patients with chronic kidney disease.

OBJECTIVES: To develop an electronic registry of patients with chronic kidney disease (CKD) treated in a nephrology practice in order to provide clinically meaningful measurement and population management to improve rates of blood pressure (BP) control. METHODS: We combined data from multiple electronic sources: the billing system, structured fields in the electronic health record (EHR), and free text physician notes using natural language processing (NLP). We also used point-of-care worksheets to capture clinical rationale. RESULTS: Nephrologist billing accurately identified patients with CKD. Using an algorithm that incorporated multiple BP readings increased the measured rate of control (130/80 mm Hg) from 37.1% to 42.3%. With the addition of NLP to capture BP readings from free text notes, the rate was 52.6%. Data from point-of-care worksheets indicated that in 52% of visits in which patients were identified as not having controlled BP, patients were actually at goal based on BP readings taken at home or on that day in the office. CONCLUSIONS: Building a method for clinically meaningful continuous performance measurement of BP control is possible, but will require data from multiple sources. Electronic measurement systems need to grow to be able to capture and process performance data from patients as well as in real-time from physicians.