Provider Patient-Sharing Networks and Multiple-Provider Prescribing of Benzodiazepines

A flexible approach to measure care coordination based on patient-sharing networks

BACKGROUND

Effective care coordination may increase clinical efficiency, but its measurement remains difficult. The established metric "care density" (CD) measures care coordination based on patient-sharing among physicians, but it may be too rigid to generalize across disorders and countries. Therefore, we propose an extension called fragmented care density (FCD), which allows varying weights for connections between different types of providers. We compare both metrics in their ability to predict hospitalizations due to schizophrenia.

METHODS

We conducted a longitudinal cohort study based on German claims data from 2014 through 2017 to predict quarterly hospital admissions. 21,016 patients with schizophrenia from the federal state Baden-Württemberg were included. CD and FCD were calculated based on patient-sharing networks. The weights of FCD were optimized to predict hospital admissions during the first year of a 24-month follow-up. Subsequently, we employed likelihood ratio tests to assess whether adding either CD or FCD improved a baseline model with control variables for the second follow-up year.

RESULTS

The inclusion of FCD significantly improved the baseline model, Χ2(1) = 53.30, p < 0.001. We found that patients with lower percentiles in FCD had an up to 21% lower hospitalization risk than those with median or higher values, whereas CD did not affect the risk.

CONCLUSIONS

FCD is an adaptive metric that can weight provider relationships based on their relevance for predicting any outcome. We used it to better understand which medical specialties need to be involved to reduce hospitalization risk for patients with schizophrenia. As FCD can be modified for different health conditions and systems, it is broadly applicable and might help to identify barriers and promoting factors for effective collaboration.

Physician patient sharing relationships within insurance plan networks

OBJECTIVE

To quantify shared patient relationships between primary care physicians (PCPs) and cardiologists and oncologists and the degree to which those relationships were captured within insurance networks.

DATA SOURCES

Secondary analysis of Vericred data on physician networks, CareSet data on physicians' shared Medicare patients, and insurance plan attributes from Health Insurance Compare. Data validation exercises used data from Physician Compare and IQVIA.

STUDY DESIGN

Cross-sectional study of the PCP-to-specialist in-network shared patient percentage (primary outcome). We also categorized networks by insurance market segment (Medicare Advantage [MA], Medicaid managed care, small-group or individually purchased), insurance plan type, and network breadth.

DATA EXTRACTION

We analyzed data on 219,982 PCPs, 29,400 cardiologists, and 22,745 oncologists who, in 2021, accepted MA (n = 941 networks), Medicaid managed care (n = 293), and individually-purchased (n = 332) and small-group (n = 501) plans.

PRINCIPAL FINDINGS

Networks captured, on average, 64.6% of PCP-cardiology shared patient ties, and 61.8% of PCP-oncologist ties. Less than half of in-network ties (44.5% and 38.9%, respectively) were among physicians with a common organizational affiliation. After adjustment for network breadth, we found no evidence of differences in the shared patient percentage across insurance market segments or networks of different types (p-value >0.05 for all comparisons). An exception was among national versus local and regional networks, where we found that national plans captured fewer shared patient ties, particularly among the narrowest networks (58.4% for national networksvs. 64.7% for local and regional networks for PCP-cardiology).

CONCLUSIONS

Given recent trends toward narrower networks, our findings underscore the importance of incorporating additional and nuanced measures of network composition to aid plan selection (for patients) and to guide regulatory oversight.

Factors associated with long-term benzodiazepine and Z-drug use across the lifespan and 5-year temporal trajectories among incident users: a Swedish nationwide register-based study

PURPOSE

Despite being discouraged by guidelines, long-term use of benzodiazepines and related Z-drugs (BZDR) remains frequent in the real-world. An improved understanding of factors associated with the transition from new to long-term BZDR use and of temporal BZDR use trajectories is needed. We aimed to assess the proportion of long-term BZDR use (> 6 months) in incident BZDR-recipients across the lifespan; identify 5-year BZDR use trajectories; and explore individual characteristics (demographic, socioeconomic and clinical) and prescribing-related factors (pharmacological properties of the initial BZDR, prescriber's healthcare level, and concurrent dispensing of other medications) associated with long-term BZDR use and distinct trajectories.

METHODS

Our nationwide register-based cohort included all BZDR-recipients in Sweden with first dispensation in 2007-2013. Trajectories of BZDR use days per year were built using group-based trajectory modelling. Cox regression and multinomial logistic regression were fitted to assess the predictors of long-term BZDR use and trajectories' membership.

RESULTS

In 930,465 incident BZDR-recipients, long-term use increased with age (20.7%, 41.0%, and 57.4% in 0-17, 18-64, and ≥ 65-year-olds, respectively). Four BZDR use trajectories emerged, labelled 'discontinued', 'decreasing', 'slow decreasing' and 'maintained'. The proportion of the 'discontinued' trajectory members was the largest in all ages, but reduced from 75.0% in the youths to 39.3% in the elderly, whereas the 'maintained' increased with age from 4.6% to 36.7%. Prescribing-related factors, in particular multiple BZDRs at initiation and concurrent dispensing of other medications, were associated with increased risks of long-term (vs short-term) BZDR use and developing other trajectories (vs 'discontinued') in all age groups.

CONCLUSIONS

The findings highlight the importance of raising awareness and providing support to prescribers to make evidence-based decisions on initiating and monitoring BZDR treatment across the lifespan.

Greater patient sharing between hospitals is associated with better outcomes for transferred emergency general surgery patients

BACKGROUND

Access to emergency surgical care has declined as the rural workforce has decreased. Interhospital transfers of patients are increasingly necessary, and care coordination across settings is critical to quality care. We characterize the role of repeated hospital patient sharing in outcomes of transfers for emergency general surgery (EGS) patients.

METHODS

A multicenter study of Wisconsin inpatient acute care hospital stays that involved transfer of EGS patients using data from the Wisconsin Hospital Association, a statewide hospital discharge census for 2016 to 2018. We hypothesized that higher proportion of patients transferred between hospitals would result in better outcomes. We examined the association between the proportion of EGS patients transferred between hospitals and patient outcomes, including in-hospital morbidity, mortality, and length of stay. Additional variables included hospital organizational characteristics and patient sociodemographic and clinical characteristics.

RESULTS

One hundred eighteen hospitals transferred 3,197 emergency general surgery patients over the 2-year study period; 1,131 experienced in-hospital morbidity, mortality, or extended length of stay (>75th percentile). Patients were 62 years old on average, 50% were female, and 5% were non-White. In the mixed-effects model, hospitals' proportion of patients shared was associated with lower odds of an in-hospital complication; specifically, when the proportion of patients shared between two hospitals doubled, the relative odds of any outcome changed by 0.85.

CONCLUSION

Our results suggest the importance of emergent relationships between hospital dyads that share patients in quality outcomes. Transfer protocols should account for established efficiencies, familiarity, and coordination between hospitals.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics

Visiting multiple prescribers is a common method for obtaining prescription opioids for nonmedical use and has played an important role in fueling the United States opioid epidemic, leading to increased drug use disorder and overdose. Recent studies show that centrality of the bipartite network formed by prescription ties between patients and prescribers of opioids is a promising indicator for drug seeking. However, node prominence in bipartite networks is typically estimated with methods that do not fully account for the two-mode topology of the underlying network. Although several algorithms have been proposed recently to address this challenge, it is unclear how these algorithms perform on real-world networks. Here, we compare their performance in the context of identifying opioid drug seeking behaviors by applying them to massive bipartite networks of patients and providers extracted from insurance claims data. We find that two variants of bipartite centrality are significantly better predictors of subsequent opioid overdose than traditional centrality estimates. Moreover, we show that incorporating non-network attributes such as the potency of the opioid prescriptions into the measures can further improve their performance. These findings can be reproduced on different datasets. Our results demonstrate the potential of bipartiteness-aware indices for identifying patterns of high-risk behavior.

Patterns of pregabalin prescribing in four German federal states: analysis of routine data to investigate potential misuse of pregabalin

OBJECTIVES

The objectives of this study were to investigate the utilisation patterns of pregabalin, to identify users potentially misusing pregabalin and to compare this group of patients to patients prescribed recommended doses of pregabalin concerning their personal characteristics and the coordination among their prescribers. Unintended coprescription of drugs with addictive potential might occur when care is insufficiently coordinated.

DESIGN

Secondary data analysis of linked data from three regional sickness funds in Germany (AOK) for the years 2014-2016.

SETTING

Ambulatory and hospital care sector in four German federal states.

METHODS

On the basis of routine data, patients who received at least three prescriptions of pregabalin were identified and classified into patients prescribed pregabalin as recommended and those dispensed with a higher than recommended dose (>600 mg/day). Social network analysis was applied to identify prescription networks and to analyse cooperation among the prescribers. With descriptive statistics and univariate statistical tests, typical characteristics of the group of patients potentially misusing pregabalin were compared with the others.

RESULTS

Among the 53 049 patients prescribed pregabalin, about 2% (877) were classified as potentially misusing pregabalin. The majority of this group was male and aged between 30 and 60 years. Of the patients misusing pregabalin, 365 (42%) had a diagnosed history of substance use disorders and 359 (41%) had been prescribed another drug with addictive potential (opioids) before. The prescribers of those patients potentially misusing pregabalin were more loosely connected within networks compared with prescribers of patients prescribed pregabalin as recommended.

CONCLUSION

This study found that patients could exceed recommended doses of pregabalin by getting prescriptions from multiple physicians. Specific patients were at increased risk of potentially misusing pregabalin, and these patients sought to obtain their prescriptions from physicians who were as loosely connected as possible. Coordination and sharing a relevant number of patients seem to be levers to avoid these problems of unintended coprescribing.

Beyond patient-sharing: Comparing physician- and patient-induced networks

The sharing of patients reflects collaborative relationships between various healthcare providers. Patient-sharing in the outpatient sector is influenced by both physicians' activities and patients' preferences. Consequently, a patient-sharing network arises from two distinct mechanisms: the initiative of the physicians on the one hand, and that of the patients on the other. We draw upon medical claims data to study the structure of one patient-sharing network by differentiating between these two mechanisms. Owing to the institutional requirements of certain healthcare systems rather following the Bismarck model, we explore different triadic patterns between general practitioners and medical specialists by applying exponential random graph models. Our findings imply deviation from institutional expectations and reveal structural realities visible in both networks.

New means, new measures: assessing prescription drug-seeking indicators over 10 years of the opioid epidemic

BACKGROUND AND AIMS

Prescription drug-seeking (PDS) from multiple prescribers is a primary means of obtaining prescription opioids; however, PDS behavior has probably evolved in response to policy shifts, and there is little agreement about how to operationalize it. We systematically compared the performance of traditional and novel PDS indicators.

DESIGN

Longitudinal study using a de-identified commercial claims database.

SETTING

United States, 2009-18.

PARTICIPANTS

A total of 318 million provider visits from 21.5 million opioid-prescribed patients.

MEASUREMENTS

We applied binary classification and generalized linear models to compare predictive accuracy and average marginal effect size predicting future opioid use disorder (OUD), overdose and high morphine milligram equivalents (MME). We compared traditional indicators of PDS to a network centrality measure, PageRank, that reflects the prominence of patients in a co-prescribing network. Analyses used the same data and adjusted for patient demographics, region, SES, diagnoses and health services.

FINDINGS

The predictive accuracy of a widely used traditional measure (N + unique doctors and N + unique pharmacies in 90 days) on OUD, overdose and MME decreased between 2009 and 2018, and performed no better than chance (50% accuracy) after 2015. Binarized PageRank measures however exhibited higher predictive accuracy than the traditional binary measures throughout 2009-2018. Continuous indicators of PDS performed better than binary thresholds, with days of Rx performing best overall with 77-93% predictive accuracy. For example, days of Rx had the highest average marginal effects on overdose and OUD: a 1 standard deviation increase in days of Rx was associated with a 6-8% [confidence intervals (CIs) = 0.058-0.061 and 0.078-0.082] increase in the probability of overdose and a 4-5% (CIs = 0.038-0.043 and 0.047-0.053) increase in the probability of OUD. PageRank performed nearly as well or better than traditional indicators of PDS, with predictive performance increasing after 2016.

CONCLUSIONS

In the United States, network-based measures appear to have increasing promise for identifying prescription opioid drug-seeking behavior, while indicators based on quantity of providers or pharmacies appear to have decreasing utility.

Defining "Doctor shopping" with Dispensing Data: A Scoping Review

BACKGROUND

"Doctor shopping" typically refers to patients that seek controlled substance prescriptions from multiple providers with the presumed intent to obtain these medications for non-medical use and/or diversion. The purpose of this scoping review is to document and examine the criteria used to identify "doctor shopping" from dispensing data in the United States.

METHODS

A scoping review was conducted on "doctor shopping" or analogous terminology from January 1, 2000 through December 31, 2020 using the Web of Science Core Collection (7 citation indices). Our search was limited to U.S. only, English-language, peer-reviewed and U.S. federal government studies. Studies without explicit "doctor shopping" criteria were excluded. Key components of these criteria included the number of prescribers and dispensers, dispensing period, and drug class (e.g., opioids).

RESULTS

Of 9,845 records identified, 95 articles met the inclusion criteria and our pool of studies ranged from years 2003 to 2020. The most common threshold-based or count definition was [≥4 Prescribers (P) AND ≥4 Dispensers (D)] (n = 12). Thirty-three studies used a 365-day detection window. Opioids alone were studied most commonly (n = 69), followed by benzodiazepines and stimulants (n = 5 and n = 2, respectively). Only 39 (41%) studies provided specific drug lists with active ingredients.

CONCLUSION

Relatively simple P × D criteria for identifying "doctor shopping" are still the dominant paradigm with the need for on-going validation. The value of P × D criteria may change through time with more diverse methods applied to dispensing data emerging.

Integrating network theory into the study of integrated healthcare

Healthcare policy in the United States (U.S.) has focused on promoting integrated healthcare to combat fragmentation (e.g., 1993 Health Security Act, 2010 Affordable Care Act). Researchers have responded by studying coordination and developing typologies of integration. Yet, after three decades, research evidence for the benefits of coordination and integration are lacking. We argue that research efforts need to refocus in three ways: (1) use social networks to study relational coordination and integrated healthcare, (2) analyze integrated healthcare at three levels of analysis (micro, meso, macro), and (3) focus on clinical integration as the most proximate impact on patient outcomes. We use examples to illustrate the utility of such refocusing and present avenues for future research.

Characteristics and Network Influence of Providers Involved in the Treatment of Patients With Chronic Back, Neck or Joint Pain in Arkansas

Increasing emphasis on guidelines and prescription drug monitoring programs highlight the role of healthcare providers in pain treatment. Objectives of this study were to identify characteristics of key players and influence of opioid prescribers through construction of a referral network of patients with chronic pain. A retrospective cohort study was performed and patients with commercial or Medicaid coverage with chronic back, neck, or joint pain were identified using the Arkansas All-Payer Claims-Database. A social network comprised of providers connected by patient referrals based on 12-months of healthcare utilization following chronic pain was constructed. Network measures evaluated were indegree and eigen (referrals obtained), betweenness (involvement), and closeness centrality (reach). Outcomes included influence of providers, opioid prescribers, and brokerage status. Exposures included provider demographics, specialties and network characteristics. There were 51,941 chronic pain patients who visited 8,110 healthcare providers. Primary care providers showed higher betweenness and closeness whereas specialists had higher indegree. Opioid providers showed higher centrality compared to non-opioid providers, which decreased with increasing volume of opioid prescribing. Non-pharmacologic providers showed significant brokerage scores. Findings from this study such as primary care providers having better reach, non-central positions of high-volume prescribers and non-pharmacologic providers having higher brokerage can aid interventional physician detailing. PERSPECTIVE: Opioid providers held central positions in the network aiding provider-directed interventions. However, high-volume opioid providers were at the borders making them difficult targets for interventions. Primary care providers had the highest reach, specialists received the most referrals and non-pharmacological providers and specialists acted as brokers between non-opioid and opioid prescribers.

Racial Disparities in Preventable Adverse Events Attributed to Poor Care Coordination Reported in a National Study of Older US Adults

BACKGROUND

Previous work found that Black patients experience worse care coordination than White patients.

OBJECTIVE

The aim was to determine if there are racial disparities in self-reported adverse events that could have been prevented with better communication.

RESEARCH DESIGN

We used data from a cross-sectional survey that was administered to participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study in 2017-2018.

SUBJECTS

REGARDS participants aged 65+ years of age who reported >1 ambulatory visits and >1 provider in the prior 12 months (thus at risk for gaps in care coordination).

MEASURES

Our primary outcome was any repeat test, drug-drug interaction, or emergency department visit or hospitalization that respondents thought could have been prevented with better communication. We used Poisson models with robust standard error to determine if there were differences in preventable events by race.

RESULTS

Among 7568 REGARDS respondents, the mean age was 77 years (SD: 6.7), 55.4% were female, and 33.6% were Black. Black participants were significantly more likely to report any preventable adverse events compared with Whites [adjusted risk ratio (aRR): 1.64; 95% confidence interval (CI): 1.42-1.89]. Specifically, Blacks were more likely than Whites to report a repeat test (aRR: 1.77; 95% CI: 1.38-2.29), a drug-drug interaction (aRR: 1.76; 95% CI: 1.46-2.12), and an emergency department visit or hospitalization (aRR: 1.45; 95% CI: 1.01-2.08).

CONCLUSIONS

Black participants were significantly more likely to report a preventable adverse event attributable to poor care coordination than White participants, independent of demographic and clinical characteristics.

The association of insurance plan characteristics with physician patient-sharing network structure

Professional and social connections among physicians impact patient outcomes, but little is known about how characteristics of insurance plans are associated with physician patient-sharing network structure. We use information from commercially insured enrollees in the 2011 Massachusetts All Payer Claims Database to construct and examine the structure of the physician patient-sharing network using standard and novel social network measures. Using regression analysis, we examine the association of physician patient-sharing network measures with an indicator of whether a patient is enrolled in a health maintenance organization (HMO) or preferred provider organization (PPO), controlling for patient and insurer characteristics and observed health status. We find patients enrolled in HMOs see physicians who are more central and densely embedded in the patient-sharing network. We find HMO patients see PCPs who refer to specialists who are less globally central, even as these specialists are more locally central. Our analysis shows there are small but significant differences in physician patient-sharing network as experienced by patients with HMO versus PPO insurance. Understanding connections between physicians is essential and, similar to previous findings, our results suggest policy choices in the insurance and delivery system that change physician connectivity may have important implications for healthcare delivery, utilization and costs.

An Examination of Prescribing Responsibilities between Psychiatrists and Primary Care Providers

Patients with comorbid mental health and chronic conditions often receive care from both psychiatrists and primary care physicians (PCPs). The introduction of multiple providers into the care process introduces opportunities for disruptions in care continuity. The purpose of this study was to explore psychiatrists' and PCPs' comfort prescribing, along with their comfort having other physician specialties prescribe medications for cardiometabolic, psychiatric, and neurological/behavioral conditions. This cross-sectional study utilized an online, validated, pilot-tested, anonymous survey to examine prescribing practices of psychiatrists and PCPs. Eligible participants included physicians with medical degrees, U.S. prescribing authority, and active patient care for ≥2 days/week. Outcomes of interest were physicians' self-comfort and cross-specialty comfort (other specialists prescribing mutual patients' medications) prescribing cardiometabolic, psychiatric, and neurological/behavioral medications. Comfort prescribing was measured using 7-point Likert scales. Discrepancies in comfort were analyzed using student's, one-sample, and paired t-tests. Multiple linear regressions examined associations between physician practice characteristics and physicians' comfort-level prescribing cardiometabolic and psychiatric medication categories. Among 50 psychiatrists and 50 PCPs, psychiatrists reported significantly lower self-comfort prescribing cardiometabolic medications (mean ± SD = 2.99 ± 1.63 vs. 6.77 ± 0.39, p < 0.001), but significantly higher self-comfort prescribing psychiatric medications (mean ± SD = 6.79 ± 0.41 vs. 6.00 ± 0.88, p < 0.001) and neurological/behavioral medications (mean ± SD = 6.48 ± 0.74 vs. 5.56 ± 1.68, p < 0.001) than PCPs. After adjusting for covariates, physician specialty was strongly associated with self-comfort prescribing cardiometabolic and psychiatric medication categories (both p < 0.001). Differences between self-comfort and cross-specialty comfort were identified. Because comfort prescribing medications differed by physician type, incorporating psychiatrists through collaborative methods with PCPs could potentially ensure comfort among physicians when initiating medications.

Benzodiazepine and Z-Drug Use in Switzerland: Prevalence, Prescription Patterns and Association with Adverse Healthcare Outcomes

PURPOSE

This study aimed to give a nationwide comprehensive picture of the prevalence and prescription patterns of benzodiazepines (BZ) and Z-drugs (ZD) in Switzerland and to analyze the association with adverse health care outcomes.

PATIENTS AND METHODS

A population-based, cross-sectional study was conducted, using a large health insurance database in Switzerland. Records from all adult patients with ≥1 prescription for a benzodiazepine and/or a Z-drug in 2018 were included. We calculated the prevalence of BZ and ZD user (extrapolated to the Swiss general population), the number of prescriptions and the type of provider (among each BZ and ZD only user). Multivariate logistic regression models were performed to estimate the association between drug prescription and the risk of hospitalization in different healthcare settings.

RESULTS

Of a total of 844'692 patients, 95'179 had ≥1 BZ and/or ZD prescription in 2018. The extrapolated one-year prevalence for the general Swiss population was 8.1% for a BZ prescription, 3.5% for a ZD prescription, and 10.5% for a BZ and/or ZD prescription, and continuously increased with age. The majority of the elderly (over 65 years) had ≥1 prescription (BZ: 51.9%; ZD: 56.9%; BZ and/or ZD: 53.5). The proportion of patients with ≥6 prescriptions per year was 23.1% for BZ only user and 35.2% for ZD only user. Most patients had ≥1 prescription from a general practitioner. Regression models showed a higher likelihood to be admitted to acute care, psychiatry, rehabilitation, or nursing home with ≥1 prescription for a benzodiazepine and/or a Z-drug.

CONCLUSION

This study is the first to give a nationwide overview of the current use of benzodiazepines and Z-drugs in Switzerland based on health insurance claims data. The results revealed a remarkably high prevalence among the general Swiss population, especially in older generations. The negative consequences of heavy BZ and ZD use are a crucial public health problem, that should be addressed.

Health Information Technology and Doctor Shopping: A Systematic Review

Doctor shopping is the practice of visiting multiple physicians to obtain multiple prescriptions. Health information technology (HIT) allows healthcare providers and patients to leverage records or shared information to improve effective care. Our research objective was to determine how HIT is being leveraged to control for doctor shopping. We analyzed articles that covered a 10-year time period from four databases and reported using preferred reporting items for systematic reviews and meta-analysis (PRISMA). We compared intervention, study design, and bias, in addition to showing intervention interactions with facilitators, barriers, and medical outcomes. From 42 articles published from six countries, we identified seven interventions, five facilitator themes with two individual observations, three barrier themes with six individual observations, and two medical outcome themes with four individual observations. Multiple HIT mechanisms exist to control for doctor shopping. Some are associated with a decrease in overdose mortality, but access is not universal or compulsory, and data sharing is sporadic. Because shoppers travel hundreds of miles in pursuit of prescription drugs, data sharing should be an imperative. Research supports leveraging HIT to control doctor shopping, yet without robust data sharing agreements, the efforts of the system are limited to the efforts of the entity with the least number of barriers to their goal. Shoppers will seek out and exploit that organization that does not require participation or checking of prescription drug monitoring programs (PDMP), and the research shows that they will drive great distances to exploit this weakest link.

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing Physician and Other Supplier Data CY 2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies.

Concurrent Opioid and Benzodiazepine Prescriptions Among Older Women Diagnosed With Breast Cancer

Guidelines recommend using caution in co-prescribing opioids with benzodiazepines, yet, in practice, the extent of concurrent prescribing is poorly understood. Notably, no population-based studies, to our knowledge, have investigated concurrent prescribing among patients with cancer. We conducted a retrospective cohort study using data from the Surveillance, Epidemiology, and End Results (SEER) database linked with Medicare claims (2012-2016) for women diagnosed with breast cancer. We used modified Poisson regression to examine predictors of any concurrent prescriptions in the year post-diagnosis and Poisson regression to examine predictors of the number of overlapping days. We found that 13.0% of the 19 267 women in our sample had concurrent prescriptions. Women who underwent more extensive treatment and those with previous use of opioids or benzodiazepines were at increased risk for concurrent prescriptions (adjusted risk ratio of previous benzodiazepine use vs no previous use = 15.05, 95% confidence interval = 13.19 to 17.19). Among women with concurrent prescriptions, overlap was most pronounced among low-income, rural, and Hispanic women (adjusted incidence rate ratio of Hispanic vs non-Hispanic white = 1.25, 95% confidence interval = 1.20 to 1.30). Our results highlight opportunities to reduce patients' unnecessary exposure to this combination.

The association between patient sharing network structure and healthcare costs

STUDY QUESTION

While physician relationships (measured through shared patients) are associated with clinical and utilization outcomes, the extent to which this is driven by local or global network characteristics is not well established. The objective of this research is to examine the association between local and global network statistics with total medical spending and utilization.

DATA SOURCE

Data used are the 2011 Massachusetts All Payer Claims Database.

STUDY DESIGN

The association between network statistics and total medical spending and utilization (using standardized prices) is estimated using multivariate regression analysis controlling for patient demographics and health status.

DATA COLLECTION

We limit the sample to continuously enrolled commercially insured patients in Massachusetts in 2011.

PRINCIPAL FINDINGS

Mean patient age was 45 years, and 56.3% of patients were female. 73.4% were covered by a health maintenance organization. Average number of visits was 5.43, with average total medical spending of $4,911 and total medical utilization of $4,252. Spending was lower for patients treated by physicians with higher degree (p<0.001), eigenvector centrality (p<0.001), clustering coefficient (p<0.001), and measures reflecting the normalized degree (p<0.001) and eigenvector centrality (p<0.001) of specialists connected to a patient's PCP. Spending was higher for patients treated by physicians with higher normalized degree, which accounts for physician specialty and patient panel size (p<0.001). Results were similar for utilization outcomes, although magnitudes differed indicating patients may see different priced physicians.

CONCLUSIONS

Generally, higher values of network statistics reflecting local connectivity adjusted for physician characteristics are associated with increased costs and utilization, while higher values of network statistics reflecting global connectivity are associated with decreased costs and utilization. As changes in the financing and delivery system advance through policy changes and healthcare consolidation, future research should examine mechanisms through which this structure impacts outcomes and potential policy responses to determine ways to reduce costs while maintaining quality and coordination of care.

WHAT THIS STUDY ADDS

It is unknown whether local and global measures of physician network connectivity associated with spending and utilization for commercially insured patients?In this social network analysis, we found generally higher values of network statistics reflecting local connectivity are associated with increased costs and utilization, while higher values of network statistics reflecting global connectivity are associated with decreased costs and utilization.Understanding how to influence local and global physician network characteristics may be important for reducing costs while maintaining quality.

Evaluation of Physician Network-Based Measures of Care Coordination Using Medicare Patient-Reported Experience Measures

BACKGROUND

There is significant promise in analyzing physician patient-sharing networks to indirectly measure care coordination, yet it is unknown whether these measures reflect patients' perceptions of care coordination.

OBJECTIVE

To evaluate the associations between network-based measures of care coordination and patient-reported experience measures.

DESIGN

We analyzed patient-sharing physician networks within group practices using data made available by the Centers for Medicare and Medicaid Services.

SUBJECTS

Medicare beneficiaries who provided responses to the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey in 2016 (data aggregated by physician group practice made available through the Physician Compare 2016 Group Public Reporting).

MAIN MEASURES

The outcomes of interest were patient-reported experience measures reflecting aspects of care coordination (CAHPS). The predictor variables of interests were physician group practice density (the number of physician pairs who share patients adjusting for the total number of physician pairs) and clustering (the extent to which sets of three physicians share patients).

KEY RESULTS

Four hundred seventy-six groups had patient-reported measures available. Patients' perception of "Clinicians working together for your care" was significantly positively associated with both physician group practice density (Est (95 % CI) = 5.07(0.83, 9.33), p = 0.02) and clustering (Est (95 % CI) = 3.73(1.01, 6.44), p = 0.007). Physician group practice clustering was also significantly positively associated with "Getting timely care, appointments, and information" (Est (95 % CI) = 4.63(0.21, 9.06), p = 0.04).

CONCLUSIONS

This work suggests that network-based measures of care coordination are associated with some patient-reported experience measures. Evaluating and intervening on patient-sharing networks may provide novel strategies for initiatives aimed at improving quality of care and the patient experience.

Co-prescription network reveals social dynamics of opioid doctor shopping

This paper examines network prominence in a co-prescription network as an indicator of opioid doctor shopping (i.e., fraudulent solicitation of opioids from multiple prescribers). Using longitudinal data from a large commercially insured population, we construct a network where a tie between patients is weighted by the number of shared opioid prescribers. Given prior research suggesting that doctor shopping may be a social process, we hypothesize that active doctor shoppers will occupy central structural positions in this network. We show that network prominence, operationalized using PageRank, is associated with more opioid prescriptions, higher predicted risk for dangerous morphine dosage, opioid overdose, and opioid use disorder, controlling for number of prescribers and other variables. Moreover, as a patient's prominence increases over time, so does their risk for these outcomes, compared to their own average level of risk. Results highlight the importance of co-prescription networks in characterizing high-risk social dynamics.

An empiric approach to identifying physician peer groups from claims data: An example from breast cancer care

OBJECTIVE

To develop an empiric approach for evaluating the performance of physician peer groups based on patient-sharing in administrative claims data.

DATA SOURCES

Surveillance, Epidemiology and End Results-Medicare linked dataset.

STUDY DESIGN

Applying social network theory, we constructed physician peer groups for patients with breast cancer. Under different assumptions of key parameter values-minimum patient volume for physician inclusion and minimum number of patients shared between physicians for a connection-we compared agreement in group membership between split samples during 2004-2006 (T1) (reliability) and agreement in group membership between T1 and 2007-2009 (T2) (stability). We also compared the results with those derived from randomly generated groups and to hospital affiliation-based groups.

PRINCIPAL FINDINGS

The sample included 142 098 patients treated by 43 174 physicians in T1 and 136 680 patients treated by 51 515 physicians in T2. We identified parameter values that resulted in a median peer group reliability of 85.2 percent (Interquartile range (IQR) [0 percent, 96.2 percent]) and median stability of 73.7 percent (IQR [0 percent, 91.0 percent]). In contrast, stability of randomly assigned peer groups was 6.2 percent (IQR [0 percent, 21.0 percent]). Median overlap of empirical groups with hospital groups was 32.2 percent (IQR [12.1 percent, 59.2 percent]).

CONCLUSIONS

It is feasible to construct physician peer groups that are reliable, stable, and distinct from both randomly generated and hospital-based groups.

Physician Care Coordination and the Use of Psychotropic Polypharmacy in the Management of Pediatric Mental Disorders

BACKGROUND

Psychotropic polypharmacy is a concern in the management of pediatric mental disorders due to the lack of pediatric data to support the practice. Although seeing multiple providers has been identified as an important predictor of polypharmacy, no study has yet assessed the effect of care coordination between providers on receipt of psychotropic polypharmacy.

OBJECTIVE

To examine the association between the intensity of care coordination within a patient's care team and the likelihood of the patient receiving multiclass psychotropic polypharmacy.

METHODS

A retrospective study was conducted using the 2013-2015 administrative claims data from a Medicaid managed care organization (Texas Children's Health Plan). Children and adolescents aged 18 years or younger with a diagnosis of a mental/behavioral disorder and receipt of psychotropic prescriptions from multiple prescribers were included in the study. Psychotropic polypharmacy was defined as the receipt of 2 or more psychotropic medications from different drug classes concurrently for 60 days or more. Care coordination was measured using social network analysis (SNA), a new technique included in the Agency for Healthcare Research and Quality Care Coordination Measures Atlas. Care density, an SNA surrogate for care coordination, was calculated as the ratio of the sum of patients shared by physician pairs within a patient's care team to the total number of physician pairs. The Andersen behavioral model was used to guide multivariate logistic regression analyses conducted to assess the association between care density and the likelihood of patients receiving psychotropic polypharmacy after controlling for predisposing and need factors.

RESULTS

A total of 24,147 children and adolescents diagnosed with a mental/behavioral disorder were identified. About 34.0% (n = 8,092) of these individuals received psychotropic medications from multiple prescribers who were either primary care physicians (PCPs) or specialists. Logistic regression analysis showed a significant association between care density and the use of psychotropic polypharmacy. However, the direction of this relationship varied depending on the composition of the patient's care team. Among patients with only PCPs involved in their care team, patients in the higher care-density group were 28% less likely to receive psychotropic polypharmacy (OR = 0.72; 95% CI = 0.62-0.96) than those in the lower care-density group. In contrast, among patients who had both PCPs and specialists involved in their care team, those in the higher care-density group were 2 times more likely to experience psychotropic polypharmacy (OR = 2.01; 95% CI = 1.68-2.40). Care density was not significantly associated with the receipt of psychotropic polypharmacy in the specialist-only group.

CONCLUSIONS

This study found significant associations between care density and prescription of psychotropic polypharmacy. This relationship varied depending on the patient's diagnosis, disease complexity, and composition of the patient's care team.

DISCLOSURES

No outside funding supported this study. The authors do not have any financial relationships or potential conflicts of interest relevant to this article to disclose. The abstract for part of this study, titled "Association Between Physician Care Coordination and the Use of Psychotropic Polypharmacy in the Management of Pediatric Mental Disorders," was selected as a silver medal abstract and was presented at the AMCP Managed Care & Specialty Pharmacy Annual Meeting 2017; March 27-30, 2017; Denver, CO.

Who diagnosed and prescribed what? Using provider details to inform observational research

PURPOSE

To describe how often patients with depression initiating antidepressants receive their depression diagnosis and prescriptions from the same provider and, when simultaneously initiating benzodiazepines, how often both prescriptions come from the same provider.

METHODS

Using a US healthcare claims database, we created a cohort of adults (18-64 years) with a depression diagnosis who initiated antidepressants. We examined concordance by provider specialty and provider identifier between (a) the first antidepressant prescription fill and most proximal depression diagnosis, and (b) the initial antidepressant and benzodiazepine prescription fills among simultaneous benzodiazepine and antidepressant initiators.

RESULTS

Among 245 166 antidepressant initiators with a recent depression diagnosis (female = 67%; median age = 39), the specialty of the provider assigning the depression diagnosis matched the antidepressant prescriber's specialty in 94% of cases with known provider details (provider identifier concordance = 93%). Concordance was higher for adults diagnosed by a general practitioner (98%) or psychiatrist (92%) than for those diagnosed by a psychologist (74%). In simultaneous new users of antidepressants and benzodiazepines (n = 19 371), both prescriptions were issued by the same provider specialty and provider identifier 94% and 93% of the time, respectively.

CONCLUSIONS

The vast majority of patients who received antidepressant prescriptions and depression diagnoses appear to have received both diagnosis and antidepressants from the same provider, suggesting that when antidepressants are issued around the time a patient is diagnosed with depression, the antidepressant was likely prescribed for depression. In addition, the great majority of patients who simultaneously initiate benzodiazepines appear to do so under the direction of one provider.

Comparison of physician networks constructed from thresholded ties versus shared clinical episodes

OBJECTIVE

To compare standard methods for constructing physician networks from patient-physician encounter data with a new method based on clinical episodes of care.

DATA SOURCE

We used data on 100% of traditional Medicare beneficiaries from 51 nationally representative geographical regions for the years 2005-2010.

STUDY DESIGN

We constructed networks of physicians based on their shared patients. In the fixed-threshold networks and adaptive-threshold networks, we included data on all patient-physician encounters to form the physician-physician ties, and then subsequently thresholded some proportion of the strongest ties. In contrast, in the episode-based approach, only those patient-physician encounters that occurred within shared clinical episodes treating specific conditions contributed towards physician-physician ties.

DATA COLLECTION/EXTRACTION METHODS

We extracted clinical episodes in the Medicare data and investigated structural properties of the patient-sharing networks of physicians, temporal dynamics of their ties, and temporal stability of network communities across the two approaches.

PRINCIPAL FINDINGS

The episode-based networks accentuated ties between primary care physicians (PCPs) and medical specialists, had ties that were more likely to reappear in the future, and appeared to have more fluid community structure.

CONCLUSIONS

Constructing physician networks around shared episodes of care is a clinically sound alternative to previous approaches to network construction that does not require arbitrary decisions about thresholding. The resulting networks capture somewhat different aspects of patient-physician encounters.

A scoping review of patient-sharing network studies using administrative data

There is a robust literature examining social networks and health, which draws on the network traditions in sociology and statistics. However, the application of social network approaches to understand the organization of health care is less well understood. The objective of this work was to examine approaches to conceptualizing, measuring, and analyzing provider patient-sharing networks. These networks are constructed using administrative data in which pairs of physicians are considered connected if they both deliver care to the same patient. A scoping review of English language peer-reviewed articles in PubMed and Embase was conducted from inception to June 2017. Two reviewers evaluated article eligibility based upon inclusion criteria and abstracted relevant data into a database. The literature search identified 10,855 titles, of which 63 full-text articles were examined. Nine additional papers identified by reviewing article references and authors were examined. Of the 49 papers that met criteria for study inclusion, 39 used a cross-sectional study design, 6 used a cohort design, and 4 were longitudinal. We found that studies most commonly theorized that networks reflected aspects of collaboration or coordination. Less commonly, studies drew on the strength of weak ties or diffusion of innovation frameworks. A total of 180 social network measures were used to describe the networks of individual providers, provider pairs and triads, the network as a whole, and patients. The literature on patient-sharing relationships between providers is marked by a diversity of measures and approaches. We highlight key considerations in network identification including the definition of network ties, setting geographic boundaries, and identifying clusters of providers, and discuss gaps for future study.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

The Impact of Provider Networks on the Co-Prescriptions of Interacting Drugs: A Claims-Based Analysis

INTRODUCTION

Multiple provider prescribing of interacting drugs is a preventable cause of morbidity and mortality, and fragmented care is a major contributing factor. We applied social network analysis to examine the impact of provider patient-sharing networks on the risk of multiple provider prescribing of interacting drugs.

METHODS

We performed a retrospective analysis of commercial healthcare claims (years 2008-2011), including all non-elderly adult beneficiaries (n = 88,494) and their constellation of care providers. Patient-sharing networks were derived based on shared patients, and care constellation cohesion was quantified using care density, defined as the ratio between the total number of patients shared by provider pairs and the total number of provider pairs within the care constellation around each patient.

RESULTS

In our study, 2% (n = 1796) of patients were co-prescribed interacting drugs by multiple providers. Multiple provider prescribing of interacting drugs was associated with care density (odds ratio per unit increase in the natural logarithm of the value for care density 0.78; 95% confidence interval 0.74-0.83; p < 0.0001). The effect of care density was more pronounced with increasing constellation size: when constellation size exceeded ten providers, the risk of multiple provider prescribing of interacting drugs decreased by nearly 37% with each unit increase in the natural logarithm of care density (p < 0.0001). Other predictors included increasing age of patients, increasing number of providers, and greater morbidity.

CONCLUSION

Improved care cohesion may mitigate unsafe prescribing practices, especially in larger care constellations. There is further potential to leverage network analytics to implement large-scale surveillance applications for monitoring prescribing safety.

Provider Connectedness to Other Providers Reduces Risk of Readmission After Hospitalization for Heart Failure

Provider interactions other than explicit care coordination, which is challenging to measure, may influence practice and outcomes. We performed a network analysis using claims data from a commercial payor. Networks were identified based on provider pairs billing outpatient care for the same patient. We compared network variables among patients who had and did not have a 30-day readmission after hospitalization for heart failure. After adjusting for comorbidities, high median provider connectedness-normalized degree, which for each provider is the number of connections to other providers normalized to the number of providers in the region-was the network variable associated with reduced odds of readmission after heart failure hospitalization (odds ratio = 0.55; 95% confidence interval [0.35, 0.86]). We conclude that heart failure patients with high provider connectedness are less likely to require readmission. The structure and importance of provider relationships using claims data merits further study.

Geographic Variations in Physician Relationships Over Time: Implications for Care Coordination

Care coordination may be more challenging when the specific physicians with whom primary care physicians (PCPs) are expected to coordinate care change over time. Using Medicare data on physician patient-sharing relationships and the Dartmouth Atlas, we explored the extent to which PCPs tend to share patients with other physicians over time. We found that 70.7% of ties between PCPs and other physicians that were present in 2012 persisted in 2013, and additional shared patients in 2012 increased the odds of being connected in 2013. Regions with higher persistent ties tended to have lower rates of emergency room visits, and regions where PCPs had more physician connections were more likely to have higher emergency room visits. The results point to potential opportunities and challenges faced by health care reforms that seek to improve coordination.

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.