Adrian H. Zai

Linking Electronic Health Record-Extracted Psychosocial Data in Real-Time to Risk of Readmission for Heart Failure

BACKGROUND Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care. OBJECTIVE We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days. METHODS We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission. RESULTS We found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source. CONCLUSIONS Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.

Causes of Short-Term Readmission After Percutaneous Coronary Intervention

Background—Rehospitalization within 30 days after an admission for percutaneous coronary intervention (PCI) is common, costly, and a future target for Medicare penalties. Causes of readmission after PCI are largely unknown. Methods and Results—To illuminate the causes of PCI readmissions, patients with PCI readmitted within 30 days of discharge between 2007 and 2011 at 2 hospitals were identified, and their medical records were reviewed. Of 9288 PCIs, 9081 (97.8%) were alive at the end of the index hospitalization. Of these, 893 patients (9.8%) were readmitted within 30 days of discharge and included in the analysis. Among readmitted patients, 341 patients (38.1%) were readmitted for evaluation of recurrent chest pain or other symptoms concerning for angina, whereas 59 patients (6.6%) were readmitted for staged PCI without new symptoms. Complications of PCI accounted for 60 readmissions (6.7%). For cases in which chest pain or other symptoms concerning for angina prompted the readmission, 21 patients (6.2%) met criteria for myocardial infarction, and repeat PCI was performed in 54 patients (15.8%). The majority of chest pain patients (288; 84.4%) underwent ≥1 diagnostic imaging test, most commonly coronary angiography, and only 9 (2.6%) underwent target lesion revascularization. Conclusions—After PCI, readmissions within 30 days were seldom related to PCI complications but often for recurrent chest pain. Readmissions with recurrent chest pain infrequently met criteria for myocardial infarction but were associated with high rates of diagnostic testing.

Lessons from Implementing a Combined Workflow–Informatics System for Diabetes Management

Shortcomings surrounding the care of patients with diabetes have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease. To address these shortcomings, we developed a diabetes registry population management application to change the way our staff manages patients with diabetes. Use of this new application has helped us coordinate the responsibilities for intervening and monitoring patients in the registry among different users. Our experiences using this combined workflow-informatics intervention system suggest that integrating a chronic disease registry into clinical workflow for the treatment of chronic conditions creates a useful and efficient tool for managing disease.

Patient Navigation for Comprehensive Cancer Screening in High-Risk Patients Using a Population-Based Health Information Technology System: A Randomized Clinical Trial.

IMPORTANCE Patient navigation (PN) to improve cancer screening in low-income and racial/ethnic minority populations usually focuses on navigating for single cancers in community health center settings. OBJECTIVE We evaluated PN for breast, cervical, and colorectal cancer screening using a population-based information technology (IT) system within a primary care network. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial conducted from April 2014 to December 2014 in 18 practices in an academic primary care network. All patients eligible and overdue for cancer screening were identified and managed using a population-based IT system. Those at high risk for nonadherence with completing screening were identified using an electronic algorithm (language spoken, number of overdue tests, no-show visit history), and randomized to a PN intervention (n = 792) or usual care (n = 820). Navigators used the IT system to track patients, contact them, and provide intense outreach to help them complete cancer screening. MAIN OUTCOMES AND MEASURES Mean cancer screening test completion rate over 8-month trial for each eligible patient, with all overdue cancer screening tests combined using linear regression models. Secondary outcomes included the proportion of patients completing any and each overdue cancer screening test. RESULTS Among 1612 patients (673 men and 975 women; median age, 57 years), baseline patient characteristics were similar among randomized groups. Of 792 intervention patients, patient navigators were unable to reach 151 (19%), deferred 246 (38%) (eg, patient declined, competing comorbidity), and navigated 202 (32%). The mean proportion of patients who were up to date with screening among all overdue screening examinations was higher in the intervention vs the control group for all cancers combined (10.2% vs 6.8%; 95% CI [for the difference], 1.5%-5.2%; P < .001), and for breast (14.7% vs 11.0%; 95% CI, 0.2%-7.3%; P = .04), cervical (11.1% vs 5.7%; 95% CI, 0.8%-5.2%; P = .002), and colon (7.6% vs 4.6%; 95% CI, 0.8%-5.2%; P = .01) cancer compared with control. The proportion of overdue patients who completed any cancer screening during follow-up was higher in the intervention group (25.5% vs 17.0%; 95% CI, 4.7%-12.7%; P < .001). The intervention group had more patients completing screening for breast (23.4% vs 16.6%; 95% CI, 1.8%-12.0%; P = .009), cervical (14.4% vs 8.6%; 95% CI, 1.6%-10.5%; P = .007), and colorectal (13.7% vs 7.0%; 95% CI, 3.2%-10.4%; P < .001) cancer. CONCLUSIONS AND RELEVANCE Patient navigation as part of a population-based IT system significantly increased screening rates for breast, cervical, and colorectal cancer in patients at high risk for nonadherence with testing. Integrating patient navigation into population health management activities for low-income and racial/ethnic minority patients might improve equity of cancer care. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT02553538.

Clinical Preventability of 30-Day Readmission After Percutaneous Coronary Intervention

Background Early readmission after PCI is an important contributor to healthcare expenditures and a target for performance measurement. The extent to which 30‐day readmissions after PCI are preventable is unknown yet essential to minimizing their occurrence. Methods and Results PCI patients readmitted to hospital at which PCI was performed within 30 days of discharge at the Massachusetts General Hospital and Brigham and Womens Hospital were identified, and their medical records were independently reviewed by 2 physicians. Each reviewer used an ordinal scale (0, not; 1, possibly; 2, probably; and 3, definitely preventable) to rate clinical preventability, and a total sum score ≥2 was considered preventable. Characteristics of preventable and unpreventable readmissions were compared, and predictors of clinical preventability were assessed by using multivariate logistic regression. Of 9288 PCIs performed, 9081 (97.8%) patients survived to initial hospital discharge and 1007 (11.1%) were readmitted to the index hospital within 30 days. After excluding repeat readmissions, 893 readmissions were reviewed. Fair agreement between physician reviewers was observed (weighted κ statistic 0.44 [95% CI 0.39 to 0.49]). After aggregation of scores, 380 (42.6%) readmissions were deemed preventable and 513 (57.4%) were deemed not preventable. Common causes of preventable readmissions included staged PCI without new symptoms (14.7%), vascular/bleeding complications of PCI (10.0%), and congestive heart failure (9.7%). Conclusions Nearly half of 30‐day readmissions after PCI may have been prevented by changes in clinical decision‐making. Focusing on these readmissions may reduce readmission rates.

Efficacy and cost-effectiveness of an automated screening algorithm in an inpatient clinical trial.

Introduction Screening and recruitment for clinical trials can be costly and time-consuming. Inpatient trials present additional challenges because enrollment is time sensitive based on length of stay. We hypothesized that using an automated prescreening algorithm to identify eligible subjects would increase screening efficiency and enrollment and be cost-effective compared to manual review of a daily admission list. Methods Using a before-and-after design, we compared time spent screening, number of patients screened, enrollment rate, and cost-effectiveness of each screening method in an inpatient diabetes trial conducted at Massachusetts General Hospital. Manual chart review (CR) involved reviewing a daily list of admitted patients to identify eligible subjects. The automated prescreening (APS) method used an algorithm to generate a daily list of patients with glucose levels ≥ 180 mg/dL, an insulin order, and/or admission diagnosis of diabetes mellitus. The census generated was then manually screened to confirm eligibility and eliminate patients who met our exclusion criteria. We determined rates of screening and enrollment and cost-effectiveness of each method based on study sample size. Results Total screening time (prescreening and screening) decreased from 4 to 2 h, allowing subjects to be approached earlier in the course of the hospital stay. The average number of patients prescreened per day increased from 13 ± 4 to 30 ± 16 (P < 0.0001). Rate of enrollment increased from 0.17 to 0.32 patients per screening day. Developing the computer algorithm added a fixed cost of US

Applying operations research to optimize a novel population management system for cancer screening

3000 to the study. Based on our screening and enrollment rates, the algorithm was cost-neutral after enrolling 12 patients. Larger sample sizes further favored screening with an algorithm. By contrast, higher recruitment rates favored individual CR. Limitations Because of the before-and-after design of this study, it is possible that unmeasured factors contributed to increased enrollment. Conclusion Using a computer algorithm to identify eligible patients for a clinical trial in the inpatient setting increased the number of patients screened and enrolled, decreased the time required to enroll them, and was less expensive. Upfront investment in developing a computerized algorithm to improve screening may be cost-effective even for relatively small trials, especially when the recruitment rate is expected to be low.

Non-visit-based cancer screening using a novel population management system.

OBJECTIVE To optimize a new visit-independent, population-based cancer screening system (TopCare) by using operations research techniques to simulate changes in patient outreach staffing levels (delegates, navigators), modifications to user workflow within the information technology (IT) system, and changes in cancer screening recommendations. MATERIALS AND METHODS TopCare was modeled as a multiserver, multiphase queueing system. Simulation experiments implemented the queueing network model following a next-event time-advance mechanism, in which systematic adjustments were made to staffing levels, IT workflow settings, and cancer screening frequency in order to assess their impact on overdue screenings per patient. RESULTS TopCare reduced the average number of overdue screenings per patient from 1.17 at inception to 0.86 during simulation to 0.23 at steady state. Increases in the workforce improved the effectiveness of TopCare. In particular, increasing the delegate or navigator staff level by one person improved screening completion rates by 1.3% or 12.2%, respectively. In contrast, changes in the amount of time a patient entry stays on delegate and navigator lists had little impact on overdue screenings. Finally, lengthening the screening interval increased efficiency within TopCare by decreasing overdue screenings at the patient level, resulting in a smaller number of overdue patients needing delegates for screening and a higher fraction of screenings completed by delegates. CONCLUSIONS Simulating the impact of changes in staffing, system parameters, and clinical inputs on the effectiveness and efficiency of care can inform the allocation of limited resources in population management.

Queuing Theory to Guide the Implementation of a Heart Failure Inpatient Registry Program

Background: Advances in information technology (IT) now permit population-based preventive screening, but the best methods remain uncertain. We evaluated whether involving primary care providers (PCPs) in a visit-independent population management IT application led to more effective cancer screening. Methods: We conducted a cluster-randomized trial involving 18 primary care practice sites and 169 PCPs from June 15, 2011, to June 14, 2012. Participants included adults eligible for breast, cervical, and/or colorectal cancer screening. In practices randomized to the intervention group, PCPs reviewed real-time rosters of their patients overdue for screening and provided individualized contact (via a letter, practice delegate, or patient navigator) or deferred screening (temporarily or permanently). In practices randomized to the comparison group, overdue patients were automatically sent reminder letters and transferred to practice delegate lists for follow-up. Intervention patients without PCP action within 8 weeks defaulted to the automated control version. The primary outcome was adjusted average cancer screening completion rates over 1-year follow-up, accounting for clustering by physician or practice. Results: Baseline cancer screening rates (80.8% vs 80.3%) were similar among patients in the intervention (n = 51,071) and comparison group (n = 52,799). Most intervention providers used the IT application (88 of 101, 87%) and users reviewed 7984 patients overdue for at least 1 cancer screening (73% sent reminder letter, 6% referred directly to a practice delegate or patient navigator, and 21% deferred screening). In addition, 6128 letters were automatically sent to patients in the intervention group (total of 12,002 letters vs 16,378 letters in comparison practices; P < .001). Adjusted average cancer screening rates did not differ among intervention and comparison practices for all cancers combined (81.6% vs 81.4%; P = .84) nor breast (82.7% vs 82.7%; P = .96), cervical (84.1% vs 84.7%; P = .60), or colorectal cancer (77.8% vs 76.2%; P = .33). Conclusions: Involving PCPs in a visit-independent population management IT application resulted in similar cancer screening rates compared with an automated reminder system, but fewer patients were sent reminder letters. This suggests that PCPs were able to identify and exclude from contact patients who would have received automated reminder letters but not undergone screening.

Assessing hospital readmission risk factors in heart failure patients enrolled in a telemonitoring program

OBJECTIVE The authors previously implemented an electronic heart failure registry at a large academic hospital to identify heart failure patients and to connect these patients with appropriate discharge services. Despite significant improvements in patient identification and connection rates, time to connection remained high, with an average delay of 3.2 days from the time patients were admitted to the time connections were made. Our objective for this current study was to determine the most effective solution to minimize time to connection. DESIGN We used a queuing theory model to simulate 3 different potential solutions to decrease the delay from patient identification to connection with discharge services. MEASUREMENTS The measures included average rate at which patients were being connected to the post discharge heart failure services program, average number of patients in line, and average patient waiting time. RESULTS Using queuing theory model simulations, we were able to estimate for our current system the minimum rate at which patients need to be connected (262 patients/mo), the ideal patient arrival rate (174 patients/mo) and the maximal patient arrival rate that could be achieved by adding 1 extra nurse (348 patients/mo). CONCLUSIONS Our modeling approach was instrumental in helping us characterize key process parameters and estimate the impact of adding staff on the time between identifying patients with heart failure and connecting them with appropriate discharge services.