A Cohort Analysis of Statin Treatment Patterns Among Small-Sized Primary Care Practices

BACKGROUND

Small-sized primary care practices, defined as practices with fewer than 10 clinicians, delivered the majority of outpatient visits in the USA. Statin therapy in high-risk individuals reduces atherosclerotic cardiovascular disease (ASCVD) events, but prescribing patterns in small primary care practices are not well known. This study describes statin treatment patterns in small-sized primary care practices and examines patient- and practice-level factors associated with lack of statin treatment.

METHODS

We conducted a retrospective cohort analysis of statin-eligible patients from practices that participated in Healthy Hearts in the Heartland (H3), a quality improvement initiative aimed at improving cardiovascular care measures in small primary care practices. All statin-eligible adults who received care in one of 53 H3 practices from 2013 to 2016. Statin-eligible adults include those aged at least 21 with (1) clinical ASCVD, (2) low-density lipoprotein cholesterol (LDL-C) ≥ 190 mg/dL, or (3) diabetes aged 40-75 and with LDL-C 70-189 mg/dL. Eligible patients with no record of moderate- to high-intensity statin prescription are defined by ACC/AHA guidelines.

RESULTS

Among the 13,330 statin-eligible adults, the mean age was 58 years and 52% were women. Overall, there was no record of moderate- to high-intensity statin prescription among 5,780 (43%) patients. Younger age, female sex, and lower LDL-C were independently associated with a lack of appropriate intensity statin therapy. Higher proportions of patients insured by Medicaid and having only family medicine trained physicians (versus having at least one internal medicine trained physician) at the practice were also associated with lower appropriate intensity statin use. Lack of appropriate intensity statin therapy was higher in independent practices than in Federally Qualified Health Centers (FQHCs) (50% vs. 40%, p value < 0.01).

CONCLUSIONS

There is an opportunity for improved ASCVD risk reduction in small primary care practices. Statin treatment patterns and factors influencing lack of treatment vary by practice setting, highlighting the importance of tailored approaches to each setting.

Planning for patient-reported outcome implementation: Development of decision tools and practical experience across four clinics

INTRODUCTION

Many institutions are attempting to implement patient-reported outcome (PRO) measures. Because PROs often change clinical workflows significantly for patients and providers, implementation choices can have major impact. While various implementation guides exist, a stepwise list of decision points covering the full implementation process and drawing explicitly on a sociotechnical conceptual framework does not exist.

METHODS

To facilitate real-world implementation of PROs in electronic health records (EHRs) for use in clinical practice, members of the EHR Access to Seamless Integration of Patient-Reported Outcomes Measurement Information System (PROMIS) Consortium developed structured PRO implementation planning tools. Each institution pilot tested the tools. Joint meetings led to the identification of critical sociotechnical success factors.

RESULTS

Three tools were developed and tested: (1) a PRO Planning Guide summarizes the empirical knowledge and guidance about PRO implementation in routine clinical care; (2) a Decision Log allows decision tracking; and (3) an Implementation Plan Template simplifies creation of a sharable implementation plan. Seven lessons learned during implementation underscore the iterative nature of planning and the importance of the clinician champion, as well as the need to understand aims, manage implementation barriers, minimize disruption, provide ample discussion time, and continuously engage key stakeholders.

CONCLUSIONS

Highly structured planning tools, informed by a sociotechnical perspective, enabled the construction of clear, clinic-specific plans. By developing and testing three reusable tools (freely available for immediate use), our project addressed the need for consolidated guidance and created new materials for PRO implementation planning. We identified seven important lessons that, while common to technology implementation, are especially critical in PRO implementation.

Risk Adjusting Health Care Provider Collaboration Networks

OBJECTIVES

The quality of hospital discharge care and patient factors (health and sociodemographic) impact the rates of unplanned readmissions. This study aims to measure the effects of controlling for the patient factors when using readmission rates to quantify the weighted edges between health care providers in a collaboration network. This improved understanding may inform strategies to reduce hospital readmissions, and facilitate quality-improvement initiatives.

METHODS

We extracted 4 years of patient, provider, and activity data related to cardiology discharge workflow. A Weibull model was developed to predict the risk of unplanned 30-day readmission. A provider-patient bipartite network was used to connect providers by shared patient encounters. We built collaboration networks and calculated the Shared Positive Outcome Ratio (SPOR) to quantify the relationship between providers by the relative rate of patient outcomes, using both risk-adjusted readmission rates and unadjusted readmission rates. The effect of risk adjustment on the calculation of the SPOR metric was quantified using a permutation test and descriptive statistics.

RESULTS

Comparing the collaboration networks consisting of 2,359 provider pairs, we found that SPOR values with risk-adjusted outcomes are significantly different than unadjusted readmission as an outcome measure (p-value = 0.025). The two networks classified the same provider pairs as high-scoring 51.5% of the time, and the same low scoring provider pairs 85.6% of the time. The observed differences in patient demographics and disease characteristics between high-scoring and low-scoring provider pairs were reduced by applying the risk-adjusted model. The risk-adjusted model reduced the average variation across each individual's SPOR scored provider connections.

CONCLUSIONS

Risk adjusting unplanned readmission in a collaboration network has an effect on SPOR-weighted edges, especially on classifying high-scoring SPOR provider pairs. The risk-adjusted model reduces the variance of providers' connections and balances shared patient characteristics between low- and high-scoring provider pairs. This indicates that the risk-adjusted SPOR edges better measure the impact of collaboration on readmissions by accounting for patients' risk of readmission.

Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification

OBJECTIVE

Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning.

METHODS

Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation.

RESULTS

EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation.

CONCLUSION

Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

Characterizing Teamwork in Cardiovascular Care Outcomes: A Network Analytics Approach

BACKGROUND

The nature of teamwork in healthcare is complex and interdisciplinary, and provider collaboration based on shared patient encounters is crucial to its success. Characterizing the intensity of working relationships with risk-adjusted patient outcomes supplies insight into provider interactions in a hospital environment.

METHODS AND RESULTS

We extracted 4 years of patient, provider, and activity data for encounters in an inpatient cardiology unit from Northwestern Medicine's Enterprise Data Warehouse. We then created a provider-patient network to identify healthcare providers who jointly participated in patient encounters and calculated satisfaction rates for provider-provider pairs. We demonstrated the application of a novel parameter, the shared positive outcome ratio, a measure that assesses the strength of a patient-sharing relationship between 2 providers based on risk-adjusted encounter outcomes. We compared an observed collaboration network of 334 providers and 3453 relationships to 1000 networks with shared positive outcome ratio scores based on randomized outcomes and found 188 collaborative relationships between pairs of providers that showed significantly higher than expected patient satisfaction ratings. A group of 22 providers performed exceptionally in terms of patient satisfaction. Our results indicate high variability in collaboration scores across the network and highlight our ability to identify relationships with both higher and lower than expected scores across a set of shared patient encounters.

CONCLUSIONS

Satisfaction rates seem to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform quality improvement strategies such as optimizing team staffing, identifying characteristics and practices of high-performing teams, developing evidence-based team guidelines, and redesigning inpatient care processes.

An Outcome-Weighted Network Model for Characterizing Collaboration

Shared patient encounters form the basis of collaborative relationships, which are crucial to the success of complex and interdisciplinary teamwork in healthcare. Quantifying the strength of these relationships using shared risk-adjusted patient outcomes provides insight into interactions that occur between healthcare providers. We developed the Shared Positive Outcome Ratio (SPOR), a novel parameter that quantifies the concentration of positive outcomes between a pair of healthcare providers over a set of shared patient encounters. We constructed a collaboration network using hospital emergency department patient data from electronic health records (EHRs) over a three-year period. Based on an outcome indicating patient satisfaction, we used this network to assess pairwise collaboration and evaluate the SPOR. By comparing this network of 574 providers and 5,615 relationships to a set of networks based on randomized outcomes, we identified 295 (5.2%) pairwise collaborations having significantly higher patient satisfaction rates. Our results show extreme high- and low-scoring relationships over a set of shared patient encounters and quantify high variability in collaboration between providers. We identified 29 top performers in terms of patient satisfaction. Providers in the high-scoring group had both a greater average number of associated encounters and a higher percentage of total encounters with positive outcomes than those in the low-scoring group, implying that more experienced individuals may be able to collaborate more successfully. Our study shows that a healthcare collaboration network can be structurally evaluated to characterize the collaborative interactions that occur between healthcare providers in a hospital setting.

Abstract 161: Heart Failure Care Transitions: A Queuing Theory Approach to Match Variable Hospital Discharge Rate With Outpatient Clinic Capacity

Background: Heart failure (HF) readmissions remain a major driver of cost and health care utilization. Timely follow-up of patients post-discharge represents an evidence-based intervention proven to reduce readmission rates. A previously unexplored characteristic of hospital discharges is variability in discharge caseload. This variability thwarts the timeliness of follow-up, negates the benefit of transition care planning and may lead to a higher risk of HF readmissions. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to determine if caseload can be determined more precisely in a manner that sufficiently accommodates HF discharge variability.

Objective: To analyze the impact of hospital discharge rate variability on outpatient clinic capacity needs using HF hospitalization discharge data and operations management approaches.

Methods: Higher risk hospitalizations requiring active transitional care heart failure management were detected using an enterprise data warehouse-supported process over the study period. Queuing theory approaches were used to model the impact of HF discharge clinic capacity on wait time to an appointment. Discharge clinic was modeled as a single 7-day follow-up appointment, with an acceptable scheduling window of 5 to 9 days post-discharge.

Results: During the study period of 100 days, 566 HF discharges were made, for a median of 5.66 discharges daily, or 39.6 discharges weekly. The distribution of daily discharges was skewed rightward (mode = 3, range = 0 to 18, standard deviation = 3.3, coefficient of variation = 0.58). Current clinic design: Providing one discharge slot for every hospital discharge (100% utilization) leads to an average wait of 18.3 days prior to an appointment, with only 31.9% of appointments scheduled within 7 days, and 38.9% of appointments scheduled within 9 days. Clinic re-design (queuing theory): Providing five extra discharge appointment slots per week (88% utilization or 13.6% excess capacity) reduces the expected waiting period to 1.1 days, with 99.8% of patients seen within 7 days, and virtually all patients seen within 9 days of discharge.

Conclusions: Deployment of queuing theory allows for a more precise quantification of needed clinical capacity to accomplish appropriate HF follow-up with a reasonable degree of certainty. Our simplified model demonstrates that variability in hospital discharge rates leads to excessive clinic wait times in the absence of a modest capacity buffer and consequently exposes patients to a higher risk of HF readmission. We show using single center HF discharge data that a 10-15% increase in capacity is needed to ensure an adequate follow-up service level. Ongoing process of care work will demonstrate if optimization of clinic load yields a significant reduction in HF readmissions.

Abstract 136: Complexity and Collaboration in Discharge Planning for Inpatient Cardiology

Background: Multidisciplinary, collaborative care improves outcomes and reduces costs but may unintentionally increase the operational overhead to manage the complexity of increasingly sophisticated teamwork.

Objective: To quantify complexity of a standardized discharge planning process by closely examining team continuity, composition, and experience for both explicitly defined, deliberately organized teams and implicitly defined, organically assembled teams.

Methods: We examined discharge planning team membership for inpatient cardiology encounters with a length of stay (LOS) greater than 48 hours for a three-year period from 2012-2014 in a large academic medical center. By constructing a co-affiliation graph from transactional EHR data, we determined the frequency of team occurrence and size. We then calculated the shared experience of teammates using a pairwise similarity metric and identified modularity, groups of healthcare personnel with dense connections, within the graph by applying a community detection algorithm.

Results: We identified 52,254 transactions for 3,213 encounters with an average LOS of 8.7 days for the time period. The standard discharge planning team accounted for 41,101 (79%) transactions, consisted of 7 team member types, and comprised 709 individual providers performing 36 activities. We identified 569 additional discharge planning team members, consisting of 22 additional provider types. When constrained to only explicitly defined teams, 404 unique teams with an average size of 5.9 members occurred with a mean of 8 (min=1, max=118) shared encounters. When unconstrained, organically assembled teams with no explicit definition occurred 3,209 times (mean=1; min=1, max=3) with an average size of 7.3 members. Single-encounters accounted for 21,107 (62%) of all provider pairs. However, over 50% of all discharges had at least 1 pair sharing over 99 encounters (median=4 pairs). Community detection found 9 modules (range: 12-275 members) among pairs with more than 5 shared encounters.

Conclusions: We have shown by deconstructing digital interactions via the EHR, a core group of providers, defined by role and activity, anchor most discharge planning teams. However, the EHR can also identify identify the 20% of teams with constantly recombining membership due to situational care; this can impose overhead when targeting team-wide process improvements, communication strategies, or educational initiatives.

Abstract 15: Quantifying Teamwork at Hospital Discharge for Readmissions Reduction: A Network Analytics Approach

Background: The vulnerable period for hospital readmission begins upon hospital discharge, a complex, interdisciplinary process that necessitates close teamwork for accurate execution of the discharge plan. Thus, better understanding of the quality of teamwork throughout the discharge process may inform strategies to reduce hospital readmissions rates. Novel methods using a network analytics approach to quantify teamwork may better characterize this critical clinical process, facilitate quality improvement (QI), and become an important tool in learning healthcare systems.

Methods: We extracted three years of patient, provider, and activity data related to discharge planning for an inpatient cardiology unit from Northwestern Medicine’s Enterprise Data Warehouse. We then created a provider-patient network to identify providers who shared patients and calculated readmissions rates for provider pairs. Using these data, we calculated a novel parameter, the Shared Positive Outcome Ratio (SPOR), an objective composite measure that quantifies the concentration of positive outcomes over a set of shared patients. To identify significant low-readmission and high-readmission collaborative relationships, we compared the observed network to 1000 sample networks containing randomized readmission values.

Results: We identified 133,927 actions distributed among 38 discharge activity types performed during 13,720 patient encounters. The collaborative network was composed of 1,542 providers including 503 nurses, 432 residents, 207 physicians, 111 physical and occupational therapists, 59 medical students, 32 dieticians, and other medical and administrative staff. The average encounter involved 4 providers performing 10 discharge-related actions. After pruning the network to include only provider pairs with 6 or more shared patients, we found that 6% of collaborative interactions had a significantly low SPOR, indicating lower than expected readmission rates. Conversely, 12% of collaborative interactions had a significantly high SPOR, indicating higher than expected readmission rates. We identified 21 providers who had a low SPOR for a significant percentage of their collaborations, indicating potential top performers in the teamwork domain.

Conclusions: Readmission rates appear to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform various QI strategies, such as optimizing team staffing, identifying high-performing teams who can share their best practices, and redesigning discharge care processes. Ongoing work on this model is focused on accurately risk-adjusting outcomes, which will increase the robustness of this method.

Abstract 152: Enterprise Data Warehouse-Supported Early Identification of Acute Decompensated Heart Failure Admissions for Efficient and Multidisciplinary Transitional Care Team Interventions

Background: Multidisciplinary transitional care teams represent a model for reducing heart failure readmissions. Within this context, early identification of patients hospitalized with acute decompensated heart failure (ADHF) permits meaningful transitional care plan development. Improving the efficiency of early identification of the higher risk ADHF patient represents an area not well studied in hospitalized heart failure (HF).

Objective: To validate the sensitivity and specificity of an enterprise data warehouse (EDW)-based strategy for early identification of patients with ADHF.

Methods: An EDW query was constructed to identify patients with ADHF based on clinical and diagnosis-related parameters, including BNP level and administration of intravenous diuretics. The EDW query was run daily; expert clinicians verified the diagnosis of ADHF based on comprehensive chart review. This classification was used to determine specificity of the query for ADHF. We computed the sensitivity of the EDW-based approach by matching query results to heart failure diagnosis related group (DRG) data and primary discharge diagnosis data from separate hospital systems.

Results: During the study period of 70 days, a total of 2354 charts were screened (33.6 charts per day). A total of 410 patients were identified by chart review as having heart failure requiring active management, for a specificity of 17.4%. Sensitivity was computed using both heart failure DRG data and primary discharge diagnosis data. Of the 114 patients discharged with a heart failure DRG (291, 292, or 293), all 114 were detected a priori by the admission EDW screen, for a sensitivity of 100%. A similar analysis conducted using HF principal diagnoses, which includes cardiac surgery-related admissions, yielded a sensitivity of 97.2%.

Conclusions: EDW-based screening of patients based on simple clinical parameters early in the hospitalization is highly sensitive for detection of ADHF hospitalizations, but specificity is low. Brief chart review by expert clinicians is rapid, and identifies a specific cohort of patients that can be targeted for multidisciplinary HF transitional care. A better delineation of risk has broad outpatient workflow implications. Ongoing process improvements will demonstrate if early identification of at-risk patients yields significant reduction in HF readmissions.

Visualizing collaborative electronic health record usage for hospitalized patients with heart failure

Objective To visualize and describe collaborative electronic health record (EHR) usage for hospitalized patients with heart failure.

Materials and methods We identified records of patients with heart failure and all associated healthcare provider record usage through queries of the Northwestern Medicine Enterprise Data Warehouse. We constructed a network by equating access and updates of a patient’s EHR to a provider-patient interaction. We then considered shared patient record access as the basis for a second network that we termed the provider collaboration network. We calculated network statistics, the modularity of provider interactions, and provider cliques.

Results We identified 548 patient records accessed by 5113 healthcare providers in 2012. The provider collaboration network had 1504 nodes and 83 998 edges. We identified 7 major provider collaboration modules. Average clique size was 87.9 providers. We used a graph database to demonstrate an ad hoc query of our provider-patient network.

Discussion Our analysis suggests a large number of healthcare providers across a wide variety of professions access records of patients with heart failure during their hospital stay. This shared record access tends to take place not only in a pairwise manner but also among large groups of providers.

Conclusion EHRs encode valuable interactions, implicitly or explicitly, between patients and providers. Network analysis provided strong evidence of multidisciplinary record access of patients with heart failure across teams of 100+ providers. Further investigation may lead to clearer understanding of how record access information can be used to strategically guide care coordination for patients hospitalized for heart failure.

Syndromic surveillance using ambulatory electronic health records

OBJECTIVE

To assess the performance of electronic health record data for syndromic surveillance and to assess the feasibility of broadly distributed surveillance.

DESIGN

Two systems were developed to identify influenza-like illness and gastrointestinal infectious disease in ambulatory electronic health record data from a network of community health centers. The first system used queries on structured data and was designed for this specific electronic health record. The second used natural language processing of narrative data, but its queries were developed independently from this health record. Both were compared to influenza isolates and to a verified emergency department chief complaint surveillance system.

MEASUREMENTS

Lagged cross-correlation and graphs of the three time series.

RESULTS

For influenza-like illness, both the structured and narrative data correlated well with the influenza isolates and with the emergency department data, achieving cross-correlations of 0.89 (structured) and 0.84 (narrative) for isolates and 0.93 and 0.89 for emergency department data, and having similar peaks during influenza season. For gastrointestinal infectious disease, the structured data correlated fairly well with the emergency department data (0.81) with a similar peak, but the narrative data correlated less well (0.47).

CONCLUSIONS

It is feasible to use electronic health records for syndromic surveillance. The structured data performed best but required knowledge engineering to match the health record data to the queries. The narrative data illustrated the potential performance of a broadly disseminated system and achieved mixed results.