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

A Scoping Review of Multilevel Patient-Sharing Network Measures in Health Services Research

Social network analysis is the study of the structure of relationships between social entities. Access to health care administrative datasets has facilitated use of "patient-sharing networks" to infer relationships between health care providers based on the extent to which they have encounters with common patients. The structure and nature of patient-sharing relationships can reflect observed or latent aspects of health care delivery systems, such as collaboration and influence. We conducted a scoping review of peer-reviewed studies that derived patient-sharing network measure(s) in the analyses. There were 134 papers included in the full-text review. We identified and created a centralized resource of 118 measures and uncovered three major themes captured by them: Influential and Key Players, Care Coordination and Teamwork, and Network Structure and Access to Care. Researchers may use this review to inform their use of patient-sharing network measures and to guide the development of novel measures.

Identifying opioid agonist treatment prescriber networks from health administrative data: A validation study

BACKGROUND

Given the growth of collaborative care strategies for people with opioid use disorder and the changing composition of the illicit drug supply, there is a need to identify and analyze clinic-level outcomes for centers prescribing opioid agonist treatment (OAT). We aimed to determine and validate whether prescriber networks, constructed with administrative data, can successfully identify distinct clinical practice facilities in Ontario, Canada.

METHODS

We executed a retrospective population-based cohort study using OAT prescription records from the Canadian Addiction Treatment Centres in Ontario, Canada between 01/01/2013 and 12/31/2020. Social network analysis was utilized to create networks with connections between physicians based on their shared OAT clients. We defined connections two different ways, by including the number of clients shared or a relative threshold on the percentage of shared OAT clients per physician. Clinics were identified using modularity maximization, with sensitivity analyses applying Louvain, Walktrap, and Label Propagation algorithms. Concordance between network-identified facilities and the (gold standard) de-identified facility-level IDs was assessed using overall, positive and negative agreement, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

RESULTS

From 144 physicians at 105 clinics with 32,842 OAT clients, we assessed 250 different versions of the created networks. The three different detection algorithms had wide variation in concordance, with ranges on sensitivity from 0.02 to 0.88 and PPV from 0.06 to 0.97. The optimal result, derived from the modularity maximization method, achieved high specificity (0.98, 95% CI: 0.98, 0.98) and NPV (0.98, 95% CI: 0.97, 0.98) and moderate PPV (0.54, 95% CI: 0.52, 0.57) and sensitivity (0.45, 95% CI: 0.43, 0.47). This scenario had an overall agreement of 0.96, negative agreement of 0.98, and positive agreement of 0.49.

CONCLUSIONS

Social network analysis can be used to identify clinics prescribing OAT in the absence of clinic-level identifiers, thus facilitating construction and comparison of clinic-level caseloads and treatment outcomes.

Potentially harmful drug-drug interactions in the therapeutic regimens of persons with spinal cord injury

OBJECTIVES

Individuals with spinal cord injury deal with multiple health complications that require them to use many medications. The purpose of this paper was to find the most common potentially harmful drug-drug interactions (DDIs) in therapeutic regimens of persons with spinal cord injury, and the risk factors associated with it. We further highlight the relevance of each of the DDIs specific to spinal cord injury population.

DESIGN

Observational design and cross-sectional analysis.

SETTING

Community; Canada.

PARTICIPANTS

Individuals with spinal cord injury (n = 108).

MAIN OUTCOME MEASURES/ANALYSIS

The main outcome was the presence of one or more potential DDIs that can lead to an adverse outcome. All the reported drugs were classified as per the World Health Organization's Anatomical Therapeutic Chemical Classification system. Twenty potential DDIs were selected for the analysis based on the most common medications prescribed to people with spinal cord injury and severity of clinical consequences. The medication lists of study participants were analyzed for selected DDIs.

RESULTS

Among the 20 potential DDIs analyzed in our sample, the top 3 prevalent DDIs were Opioids + Skeletal Muscle Relaxants, Opioids + Gabapentinoids, and Benzodiazepines + ≥ 2 other central nervous system (CNS)-active drugs. Of the total sample of 108 respondents, 31 participants (29%) were identified with having at least one potential DDI. The risk of having a potential DDI was highly associated with polypharmacy, though no associations were found between the presence of a drug interaction and age, sex, level of injury, time since injury, or cause of injury among the study sample.

CONCLUSION

Almost three out of ten individuals with spinal cord injury were at risk of having a potentially harmful drug interaction. Clinical and communication tools are needed that facilitate identification and elimination of harmful drug combinations in the therapeutic regimens of patients with spinal cord injury.

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.

Can peer effects explain prescribing appropriateness? a social network analysis

BACKGROUND

Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices' connectedness to peers and their prescribing performance in two German regions.

METHODS

We first mapped physician practice networks using links established between two practices that share 8 or more patients; we calculated network-level (density, average path length) and node-level measures (degree, betweenness, eigenvector). We defined prescribing performance as the total number of inappropriate medications prescribed or appropriate medications not prescribed (PIMs) to senior patients (over the age of 65) during the calendar year 2016. We used FORTA (Fit fOR The Aged) algorithm to classify medication appropriateness. Negative binomial regression models estimate the association between node-level measures and prescribing performance of physician practices controlling for patient comorbidity, provider specialization, percentage of seniors in practice, and region. We conducted two sensitivity analyses to test the robustness of our findings - i) limiting the network mapping to patients younger than 65; ii) limiting the network ties to practices that share more than 25 patients.

RESULTS

We mapped two patient-sharing networks including 436 and 270 physician practices involving 28,508 and 20,935 patients and consisting of 217,126 and 154,274 claims in the two regions respectively. Regression analyses showed a practice's network connectedness as represented by degree, betweenness, and eigenvector centrality, is significantly negatively associated with prescribing performance (degree-bottom vs. top quartile aRR = 0.04, 95%CI: 0.035,0.045; betweenness-bottom vs. top quartile aRR = 0.063 95%CI: 0.052,0.077; eigenvector-bottom vs. top quartile aRR = 0.039, 95%CI: 0.034,0.044).

CONCLUSIONS

Our study provides evidence that physician practice prescribing performance is associated with their peer connections and position within their network. We conclude that practices occupying strategic positions at the edge of networks with advantageous access to novel information are associated with better prescribing outcomes, whereas highly connected practices embedded in insulated information environments are associated with poor prescribing performance.

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.

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.

Prevalence of potentially harmful multidrug interactions on medication lists of elderly ambulatory patients

Background

It has been hypothesized that polypharmacy may increase the frequency of multidrug interactions (MDIs) where one drug interacts with two or more other drugs, amplifying the risk of associated adverse drug events (ADEs). The main objective of this study was to determine the prevalence of MDIs in medication lists of elderly ambulatory patients and to identify the medications most commonly involved in MDIs that amplify the risk of ADEs.

Methods

Medication lists stored in the electronic health record (EHR) of 6,545 outpatients ≥60 years old were extracted from the enterprise data warehouse. Network analysis identified patients with three or more interacting medications from their medication lists. Potentially harmful interactions were identified from the enterprise drug-drug interaction alerting system. MDIs were considered to amplify the risk if interactions could increase the probability of ADEs.

Results

MDIs were identified in 1.3 % of the medication lists, the majority of which involved three interacting drugs (75.6 %) while the remainder involved four (15.6 %) or five or more (8.9 %) interacting drugs. The average number of medications on the lists was 3.1 ± 2.3 in patients with no drug interactions and 8.6 ± 3.4 in patients with MDIs. The prevalence of MDIs on medication lists was greater than 10 % in patients prescribed bupropion, tramadol, trazodone, cyclobenzaprine, fluoxetine, ondansetron, or quetiapine and greater than 20 % in patients prescribed amiodarone or methotrexate. All MDIs were potentially risk-amplifying due to pharmacodynamic interactions, where three or more medications were associated with the same ADE, or pharmacokinetic, where two or more drugs reduced the metabolism of a third drug. The most common drugs involved in MDIs were psychotropic, comprising 35.1 % of all drugs involved. The most common serious potential ADEs associated with the interactions were serotonin syndrome, seizures, prolonged QT interval and bleeding.

Conclusions

An identifiable number of medications, the majority of which are psychotropic, may be involved in MDIs in elderly ambulatory patients which may amplify the risk of serious ADEs. To mitigate the risk, providers will need to pay special attention to the overlapping drug-drug interactions which result in MDIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02594-z.

Comparison of potential psychiatric drug interactions in six drug interaction database programs: A replication study after 2 years of updates

OBJECTIVE

Drug interaction database programs are a fundamental clinical tool. In 2018, we compared the category of potential drug-drug interaction (DDI) provided by six drug interaction database programs for 100 drug interaction pairs including psychiatric drugs, and found the category often differed. This study replicated the comparison in 2020 after 2 years of updates to all six drug interaction database programs.

METHODS

The 100 drug pairs included 94 different drugs: 67 pairs with a psychiatric and non-psychiatric drug, and 33 pairs with two psychiatric drugs. The assigned category of potential DDI for the drug pairs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

RESULTS

Despite 67 updates involving 46 of the 100 drug pairs, differences remained. The overall percent agreement among the six drug interaction database programs for the category of potential DDI was 67%. The interrater agreement results did not change. The Fleiss kappa overall interrater agreement was fair. The kappa agreement for a drug pair with any severe category rating was substantial, and the kappa agreement for a drug pair with any major category rating was fair.

CONCLUSIONS

Physicians should be aware of the inconsistency among drug interaction database programs in the category of potential DDI for drug pairs including psychiatric drugs. Additionally, the category of potential DDI for a drug pair may change over time. This study highlights the importance of ongoing international efforts to standardize methods used to define and classify potential DDI.

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.

Tie Decay and Dissolution: Contentious Prescribing Practices in the Prescription Drug Epidemic

Although a substantial body of work has investigated drivers of tie formation, there is growing interest in understanding why relationships decay or dissolve altogether. The networks literature has tended to conceptualize tie decay as driven by processes similar to those underlying tie formation. Yet information that is revealed through ongoing interactions can exert different effects on tie formation and tie decay. This paper investigates how tie decay and tie formation processes differ by focusing on contentious practices. To the extent that information about dissimilarities in contentious practices is learned through ongoing interactions, it can exert diverging effects on tie formation and tie decay. Using a longitudinal data set of 141,543 physician dyads, we find that differences in contentious prescribing led ties to weaken or dissolve altogether but did not affect tie formation. The more contentious the practice and the more information available about the practice, the stronger the effect on tie decay and dissolution. Collectively, these findings contribute to a more nuanced understanding of relationship evolution as an unfolding process through which deeper-level differences are revealed and shape the outcome of the tie.

Medication management for complex patients in primary care: application of a remote, asynchronous clinical pharmacist model

Purpose: Drug therapy problems impact about one-third of US adults, and these issues are likely to continue to worsen as the population of aging Americans increases. The objective of this study is to assess the feasibility of a remotely delivered Comprehensive Medication Management (CMM) for primary practice patients who are polypharmatic and at high risk for drug therapy problems.Methods: Using medical and prescription claims data, a list of Medicare Advantage beneficiaries at high risk for drug therapy problems was identified. Participants were enrolled in a 6-month CMM program from February - November 2020. In the program, their existing drug therapy was assessed by a pharmacist, Drug therapy problems were identified and resolved. A Collaborative Practice Agreement allowed the pharmacists to make prescription changes as needed.Results: Eighty-three percent (202) of contacted individuals agreed to participate in the study. All participants were on five medications or more, and 71% were on more than eight. A clinical pharmacist found that 86% of participants had a drug therapy problem according to classification criteria. Seventy-nine percent of all drug therapy problems identified were resolved upon completion of the study.Conclusion: The findings of this study suggest that engagement of a remote clinical pharmacist can contribute to efficient resolution of most drug therapy problems identified in a primary care population. A service model using remote pharmacist services may be an effective means of improving team-based primary care medication management for this population.

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.

The Prevalence of Selected Potential Drug-Drug Interactions of Analgesic Drugs and Possible Methods of Preventing Them: Lessons Learned From the Analysis of the Real-World National Database of 38 Million Citizens of Poland

Introduction: Drug-drug interactions may lead to poor health outcomes, as well as increased costs and utilization of healthcare services. Unfortunately, real-world data continuously prove high prevalence of potential drug-drug interactions (pDDIs) worldwide. Among identified drivers, ageing, multimorbidity and polypharmacy play a very important role. With these factors being widespread, the need for implementation of strategies minimizing the burden of pDDIs becomes an urgency. This, however, requires a better understanding of the prevalence of pDDIs and the underlying causative factors.

Aim of study: To assess the real-world prevalence of pDDIs and its characteristics in the general population of Poland, using analgesic drugs as a model, and to find out whether pDDIs are caused by prescribing coming from the very same prescribers (co-prescribing).

Methods: A retrospective analysis of the 2018 dispensation data of the National Health Fund (NHF) - the only Polish public healthcare payer organization with nationwide coverage. We searched for selected pDDIs of non-steroidal anti-inflammatory drugs (NSAIDs) with antihypertensives, other NSAIDs (double use), oral glucocorticoids, oral anticoagulants, selective serotonin reuptake inhibitors (SSRIs), serotonin–norepinephrine reuptake inhibitors (SNRIs), and antiplatelet drugs; as well as opioides with SSRIs, SNRIs, gabapentinoids, and benzodiazepines. A pDDI was deemed present if two drugs standing in a possible conflict were dispensed within the same calendar month.

Results: Out of 38.4 million citizens of Poland, 23.3 million were dispensed prescribed drugs reimbursed by NHF in 2018. In this cohort, we have identified 2,485,787 cases of analgesic drug pDDIs, corresponding with 6.47% of the Polish population. Out of these, the most prevalent pDDI was caused by “NSAIDs + antihypertensives” (1,583,575 cases, i.e., 4.12% of the Polish population), followed by “NSAIDs + NSAIDs” (538,640, 1.40%) and “NSAIDs + glucocorticoids” (213,504, 0.56%). The most persistent pDDIs among those studied were caused by “Opioids + Gabapentinoids” (2.19, 95%CI: 2.16–2.22 months). On average, 76.63% of all cases of pDDIs were caused by drugs prescribed by the very same prescribers.

Conclusion: Based on high-quality, nationwide data, we have found a high prevalence of analgesic drugs-related pDDIs in Poland. Over ¾ of the identified pDDIs were caused by co-prescribing, i.e., prescriptions issued by the same prescribers. The significance of the problem, illustrated with our findings on analgesic drugs-related pDDIs in Poland, deserves much more scientific and policymaker attention.

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.

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

Background Patients with bipolar disorder frequently experience polypharmacy, putting them at risk for clinically significant drug-drug interactions (DDI). Online drug interaction database programs are used to alert physicians, but there are no internationally recognized standards to define DDI. This study compared the category of potential DDI returned by 6 commercial drug interaction database programs for drug interaction pairs involving drugs commonly prescribed for bipolar disorder.

Methods The category of potential DDI provided by 6 drug interaction database programs (3 subscription, 3 open access) was obtained for 125 drug interaction pairs. The pairs involved 103 drugs (38 psychiatric, 65 nonpsychiatric); 88 pairs included a psychiatric and nonpsychiatric drug; 37 pairs included 2 psychiatric drugs. Every pair contained at least 1 mood stabilizer or antidepressant. The category provided by 6 drug interaction database programs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

Results For the 125 drug pairs, the overall percent agreement among the 6 drug interaction database programs was 60%; the Fleiss kappa agreement was slight. For drug interaction pairs with any category rating of severe (contraindicated), the kappa agreement was moderate. For drug interaction pairs with any category rating of major, the kappa agreement was slight.

Conclusion There is poor agreement among drug interaction database programs for the category of potential DDI involving psychiatric drugs. Drug interaction database programs provide valuable information, but the lack of consistency should be recognized as a limitation. When assistance is needed, physicians should check more than 1 drug interaction database program.

Use of Medicare Data to Identify Team-based Primary Care: Is it Possible?

BACKGROUND

It is unclear whether Medicare data can be used to identify type and degree of collaboration between primary care providers (PCPs) [medical doctors (MDs), nurse practitioners, and physician assistants] in a team care model.

METHODS

We surveyed 63 primary care practices in Texas and linked the survey results to 2015 100% Medicare data. We identified PCP dyads of 2 providers in Medicare data and compared the results to those from our survey. Sensitivity, specificity, and positive predictive value (PPV) of dyads in Medicare data at different threshold numbers of shared patients were reported. We also identified PCPs who work in the same practice by Social Network Analysis (SNA) of Medicare data and compared the results to the surveys.

RESULTS

With a cutoff of sharing at least 30 patients, the sensitivity of identifying dyads was 27.8%, specificity was 91.7%, and PPV 72.2%. The PPV was higher for MD-nurse practitioner/physician assistant pairs (84.4%) than for MD-MD pairs (61.5%). At the same cutoff, 90% of PCPs identified in a practice from the survey were also identified by SNA in the corresponding practice. In 5 of 8 surveyed practices with at least 3 PCPs, about ≤20% PCPs identified in the practices by SNA of Medicare data were not identified in the survey.

CONCLUSIONS

Medicare data can be used to identify shared care with low sensitivity and high PPV. Community discovery from Medicare data provided good agreement in identifying members of practices. Adapting network analyses in different contexts needs more validation studies.

Receipt of Promotional Payments at the Individual and Physician Network Level Associated with Higher Branded Antipsychotic Prescribing Rates

Pharmaceutical promotion can lead to market size expansion, which is beneficial if previously untreated patients access treatment but deleterious if it leads to overuse, an area of concern for second generation antipsychotics (SGA). We contribute to a growing body of work suggesting that networks of social and professional relationships shape prescribing behavior. We examined 88,439 Medicare Part D prescribing physicians, finding that promotion is associated with SGA market size expansion (elasticity: 0.062) and that network-level promotional activity is associated with network members' branded product prescribing. Research on the effects of promotion should account for its effects in prescribers' networks.

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.

A comparison of potential psychiatric drug interactions from six drug interaction database programs

Harmful drug-drug interactions (DDI) frequently include psychiatric drugs. Drug interaction database programs are viewed as a primary tool to alert physicians of potential DDI, but may provide different results as there is no standard to define DDI. This study compared the category of potential DDI provided by 6 commercial drug interaction database programs (3 subscription, 3 open access) for 100 drug interaction pairs. The pairs involved 94 different drugs; 67 included a psychiatric and non-psychiatric drug, and 33 included two psychiatric drugs. The category assigned to the potential DDI by the 6 programs was compared using percent agreement and Fleiss' kappa interrater reliability measure. The overall percent agreement for the category of potential DDI for the 100 drug interaction pairs was 66%. The Fleiss kappa overall interrater agreement was fair. The kappa agreement was substantial for interaction pairs with any severe category rating, and fair for interaction pairs with any major category rating. The category of potential DDI for drug interaction pairs including psychiatric drugs often differs among drug interaction database programs. Modern technology allows easy access to several interaction database programs. When assistance from a drug interaction database program is needed, the physician should check more than one program.

Patient-Sharing Relations in the Treatment of Diabetes and Their Implications for Health Information Exchange: Claims-Based Analysis

Background

Health information exchange (HIE) among care providers who cooperate in the treatment of patients with diabetes mellitus (DM) has been rated as an important aspect of successful care. Patient-sharing relations among care providers permit inferences about corresponding information-sharing relations.

Objectives

This study aimed to obtain information for an effective HIE platform design to be used in DM care by analyzing patient-sharing relations among various types of care providers (ToCPs), such as hospitals, pharmacies, and different outpatient specialists, within a nationwide claims dataset of Austrian DM patients. We focus on 2 parameters derived from patient-sharing networks: (1) the principal HIE partners of the different ToCPs involved in the treatment of DM and (2) the required participation rate of ToCPs in HIE platforms for the purpose of effective communication.

Methods

The claims data of 7.9 million Austrian patients from 2006 to 2007 served as our data source. DM patients were identified by their medication. We established metrics for the quantification of our 2 parameters of interest. The principal HIE partners were derived from the portions of a care provider’s patient-sharing relations with different ToCPs. For the required participation rate of ToCPs in an HIE platform, we determine the concentration of patient-sharing relations among ToCPs. Our corresponding metrics are derived in analogy from existing work for the quantification of the continuity of care.

Results

We identified 324,703 DM patients treated by 12,226 care providers; the latter were members of 16 ToCPs. On the basis of their score for 2 of our parameters, we categorized the ToCPs into low, medium, and high. For the most important HIE partner parameter, pharmacies, general practitioners (GPs), and laboratories were the representatives of the top group, that is, our care providers shared the highest numbers of DM patients with these ToCPs. For the required participation rate of type of care provide (ToCP) in HIE platform parameter, the concentration of DM patient-sharing relations with a ToCP tended to be inversely related to the ToCPs member count.

Conclusions

We conclude that GPs, pharmacies, and laboratories should be core members of any HIE platform that supports DM care, as they are the most important DM patient-sharing partners. We further conclude that, for implementing HIE with ToCPs who have many members (in Austria, particularly GPs and pharmacies), an HIE solution with high participation rates from these ToCPs (ideally a nationwide HIE platform with obligatory participation of the concerned ToCPs) seems essential. This will raise the probability of HIE being achieved with any care provider of these ToCPs. As chronic diseases are rising because of aging societies, we believe that our quantification of HIE requirements in the treatment of DM can provide valuable insights for many industrial countries.

Evaluating Breast Cancer Care Coordination at a Rural National Cancer Institute Comprehensive Cancer Center Using Network Analysis and Geospatial Methods

BACKGROUND

Variation in cancer care coordination may affect care quality and patient outcomes. We sought to characterize the impact of geographic access to and dispersion of cancer care providers on variation in care coordination.

METHODS

Using electronic health record data from 2,507 women diagnosed with breast cancer at a National Cancer Institute Comprehensive Cancer Center from April 2011 to September 2015, a breast cancer patient-sharing physician network was constructed. Patient "care networks" represent the subnetworks of physicians with whom the focal patient had a clinical encounter. Patient care networks were analyzed to generate two measures of care coordination, care density (ratio of observed vs. potential connections between physicians), and clustering (extent to which physicians form connected triangles).

RESULTS

The breast cancer physician network included 667 physicians. On average, the physicians shared patients with 12 other physicians. Patients saw an average of 8 physicians during active treatment. In multivariable models adjusting for patient sociodemographic and clinical characteristics, we observed that greater travel burden (>2 hours) and lower geographic dispersion were associated with higher care density (P < 0.05 and P < 0.001, respectively) but lower care network clustering (P < 0.05).

CONCLUSIONS

Variation in network-based measures of care coordination is partially explained by patient travel burden and geographic dispersion of care.

IMPACT

Improved understanding of factors driving variation in patient care networks may identify patients at risk of receiving poorly coordinated cancer care.

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.