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Importance: US hospitals report data on many health care quality metrics to government and independent health care rating organizations, but the annual cost to acute care hospitals of measuring and reporting quality metric data, independent of resources spent on quality interventions, is not well known., Objective: To evaluate externally reported inpatient quality metrics for adult patients and estimate the cost of data collection and reporting, independent of quality-improvement efforts., Design, Setting, and Participants: Retrospective time-driven activity-based costing study at the Johns Hopkins Hospital (Baltimore, Maryland) with hospital personnel involved in quality metric reporting processes interviewed between January 1, 2019, and June 30, 2019, about quality reporting activities in the 2018 calendar year., Main Outcomes and Measures: Outcomes included the number of metrics, annual person-hours per metric type, and annual personnel cost per metric type., Results: A total of 162 unique metrics were identified, of which 96 (59.3%) were claims-based, 107 (66.0%) were outcome metrics, and 101 (62.3%) were related to patient safety. Preparing and reporting data for these metrics required an estimated 108 478 person-hours, with an estimated personnel cost of $5 038 218.28 (2022 USD) plus an additional $602 730.66 in vendor fees. Claims-based (96 metrics; $37 553.58 per metric per year) and chart-abstracted (26 metrics; $33 871.30 per metric per year) metrics used the most resources per metric, while electronic metrics consumed far less (4 metrics; $1901.58 per metric per year)., Conclusions and Relevance: Significant resources are expended exclusively for quality reporting, and some methods of quality assessment are far more expensive than others. Claims-based metrics were unexpectedly found to be the most resource intensive of all metric types. Policy makers should consider reducing the number of metrics and shifting to electronic metrics, when possible, to optimize resources spent in the overall pursuit of higher quality.

Background: There are limited data on the treatment patterns, health care resource utilization (HRU), survival outcomes, and medical costs among Medicare beneficiaries newly diagnosed with peripheral T-cell lymphoma (PTCL)., Patients and Methods: This was a retrospective analysis of data from the Medicare Fee-For-Service claims database using the 100% sample of the Medicare research identifiable files. Patients identified for analysis were aged ≥ 65 years and had received a PTCL diagnosis between January 2011 and December 2017. Outcomes included patient characteristics, HRU, direct all-cause and PTCL-specific health care costs, treatment patterns, and overall survival. Patients were followed until disenrollment, death, or end of the study period., Results: Overall, 2551 patients with PTCL were included, among whom 37% had ≥ 1 emergency department visit and 42% had ≥ 1 hospitalization during the pre-index period. During follow-up (median, 2.0 years), 70% of patients were hospitalized at least once (mean length of stay, 1.34 days); 22% advanced to hospice care. A total of 1593 patients received ≥ 1 identifiable treatment regimen post index, of whom 26% received CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone) and 3% CHOEP (CHOP plus etoposide), whereas 71% received other regimens. The median overall survival among patients receiving identifiable therapy was 4.6 years. The mean adjusted per-person-per-month all-cause costs among the overall PTCL cohort during follow-up were $5930; the mean disease-related costs were $2384. Costs were driven primarily by hospitalizations (38%) and outpatient services (28%)., Conclusions: Medicare beneficiaries newly diagnosed with PTCL have high HRU and cost burden, with no evident standard of care in real-world practice., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims-type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims-type data elements (including ICD-9/ICD-10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham-validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in-patient or two out-patient diagnosis codes of ICD-9 428.3X or ICD-10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT-proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD-9 428.0, 428.3X, and 428.9/ICD-10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code-only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real-world use of the computable phenotypes, any one in-patient or out-patient code of ICD-9 428/ICD-10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in-patient or two out-patient instances of ICD-9 428.0, 428.9, or 428.3X/ICD-10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF., (© 2020 The Authors. Pharmacology Research & Perspectives published by John Wiley & Sons Ltd, British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics.)

Background: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs., Objectives: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims., Methods: We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman's coefficient (SC) after adjusting for age and sex., Results: The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels., Conclusions: Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts., Disclosures: No outside funding supported this study. The authors have nothing to disclose. The abstract of this work was presented at INFORMS Healthcare Conference, held on July 27-29, 2019, in Cambridge, MA.

Coarsened exact matching (CEM) is a matching method proposed as an alternative to other techniques commonly used to control confounding. We compared CEM with 3 techniques that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching, and fine stratification by propensity score (FS). We evaluated confounding control and effect-estimate precision using insurance claims data from the Pharmaceutical Assistance Contract for the Elderly (1999-2002) and Medicaid Analytic eXtract (2000-2007) databases (United States) and from simulated claims-based cohorts. CEM generally achieved the best covariate balance. However, it often led to high bias and low precision of the risk ratio due to extreme losses in study size and numbers of outcomes (i.e., sparse data bias)-especially with larger covariate sets. FS usually was optimal with respect to bias and precision and always created good covariate balance. Propensity score matching usually performed almost as well as FS, especially with higher index exposure prevalence. The performance of Mahalanobis distance matching was relatively poor. These findings suggest that CEM, although it achieves good covariate balance, might not be optimal for large claims-database studies with rich covariate information; it might be ideal if only a few (<10) strong confounders must be controlled., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2019. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Aim: Haemophilia A (HA) is a male-predominant disorder, yet women and girls can have factor VIII (FVIII) deficiency with bleeding events requiring treatment. This study aimed to identify and characterize female patients with HA., Methods: Administrative claims dated 01 January 2012-31 July 2016 were accessed for patients with 18 months' coverage by commercial or Medicare Advantage with Part D insurance. Patients were included by HA diagnoses or treatments and/or bleeding-related diagnoses or procedures, and excluded by haemophilia B or qualitative platelet disorder diagnoses. A sample of charts was examined for bleeding history, HA therapies and bleeding treatments. All-cause healthcare utilization and costs were also described., Results: Among 353 patients meeting initial inclusion criteria, 86 charts were procured, with 8 patients identified as having HA. Their mean age was 60 ± 17 years and most were Medicare-insured. The mean Charlson Comorbidity Index score was 2.50 ± 2.56; the most prevalent comorbid conditions involved coagulation/haemorrhage, fluid/electrolyte balance and non-traumatic joint disorders. Over 18 months, a mean of 54 ambulatory visits and 120 pharmacy fills were observed; mean medical costs were $86 694 and pharmacy costs were $25 396., Conclusions: Identifying females with HA is challenging using healthcare claims, because diagnostic nomenclature is unclear for female patients treated for bleeding events. Although chart abstraction enhanced claims data, very few female patients were identified with HA. Nevertheless, even in a small sample, sizeable burden in comorbidity and healthcare use was observed. Improved nomenclature and coding for HA diagnoses for women and girls is key to improving research and treatment., (© 2020 John Wiley & Sons Ltd.)

Purpose: The CHA 2 DS 2 -VaSc and HAS-BLED risk scores are commonly used in the studies of oral anticoagulants (OACs). The best ways to map these scores to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes is unclear, as is how they perform in various types of OAC users. We aimed to assess the distributions of CHA 2 DS 2 -VaSc and HAS-BLED scores and C-statistics for outcome prediction in the ICD-10-CM era using different mapping strategies., Methods: We compared the distributions of CHA 2 DS 2 -VaSc and HAS-BLED scores from various mapping strategies in atrial fibrillation patients before, during, and after ICD-10-CM transition. We estimated the C-statistics predicting the 90-day risk of hospitalized stroke (for CHA 2 DS 2 -VaSc) or hospitalized bleeding (for HAS-BLED) in patients identified at least 6 months after the ICD-10-CM transition, overall and by anticoagulant type., Results: Forward-backward mapping produced higher CHA 2 DS 2 -VaSc and HAS-BLED scores in the ICD-10-CM era compared to the ICD-9-CM era: the mean difference was 0.074 (95% confidence interval 0.064-0.085) for CHA 2 DS 2 -VaSc and 0.055 (0.048-0.062) for HAS-BLED. Both scores had higher C-statistics in patients taking no OACs (0.697 [0.677-0.717] for CHA 2 DS 2 -VaSc; 0.719 [0.702-0.737] for HAS-BLED) or direct OACs (0.695 [0.654-0.735] for CHA 2 DS 2 -VaSc; 0.700 [0.673-0.728] for HAS-BLED) than those taking warfarin (0.655 [0.613-0.697] for CHA 2 DS 2 -VaSc; 0.663 [0.6320.695] for HAS-BLED)., Conclusions: Existing mapping strategies generally preserved the distributions of CHA 2 DS 2 -VaSc and HAS-BLED scores after ICD-10-CM transition. Both scores performed better in patients on no OACs or direct OACs than patients on warfarin., (© 2020 John Wiley & Sons Ltd.)

Purpose: An increasing number of new medications are being developed and approved for psoriatic arthritis (PsA). To generate real-world evidence on comparative safety and effectiveness of these drugs, a claims-based algorithm that can accurately identify PsA is greatly needed., Methods: To identify patients with PsA, we developed seven claims-based algorithms based on a combination of diagnosis codes and medication dispensing using the claims data from Medicare parts A/B/D linked to electronic medical records (2012-2014). Two physicians independently conducted a chart review using the treating physician's diagnosis of PsA as the gold standard. We calculated the positive predictive value (PPV) and 95% confidence intervals of each algorithm., Results: Of the total 2157 records identified by the seven algorithms, 45% of the records had relevant clinical data to determine the presence of PsA. The PPV of the algorithms ranged from 75.2% (algorithm 1: ≥2 diagnosis codes for PsA and ≥1 diagnosis code for psoriasis) to 88.6% (algorithm 7: ≥2 diagnosis codes for PsA with ≥1 code by rheumatologist and ≥1 dispensing for PsA medication). Having ≥2 diagnosis codes and ≥1 dispensing for PsA medications (algorithm 6) also had PPV of 82.4%., Conclusions: All seven claims-based algorithms demonstrated a moderately high PPV of 75% to 89% in identifying PsA. The use of ≥2 diagnosis codes plus ≥1 prescription claim for PsA appears to be a valid and efficient tool in identifying PsA patients in the claims data, while broader algorithms based on diagnoses without a prescription claim also have reasonably good PPVs., (© 2019 John Wiley & Sons Ltd.)

Background and Objectives: In Japan, polypharmacy reduction policy, which reduces the reimbursement of medical cost, was introduced to address unnecessary psychotropic polypharmacy. The rule was applied to the prescriptions of three or more anxiolytics or three or more hypnotics in the policy introduced in 2012. The prescriptions of four or more antidepressants or four or more antipsychotics were added to the rule in the policy revised in 2014. Furthermore, the prescriptions of three or more drugs of anxiolytics, hypnotics, antidepressants, or antipsychotics were subject to the reduction criteria of the policy revision in 2016. Benzodiazepine receptor agonists (BZs) are classified both into anxiolytics and hypnotics, and the reduction rule was not applied to the category of BZs before April 2018. This study aimed to examine the effect of the policy on the prescriptions of four drug categories as well as BZs from the point of view of the number of drugs and doses., Methods: This was a retrospective observational study using a large-scale Japanese health insurance claims database. Patients who were prescribed at least one psychotropic drug (anxiolytic, hypnotic, antidepressant, or antipsychotic) during the study period (from April 2011 to March 2017) were selected. Segmented regression analysis was used to analyze the proportions of patients with three or more or four or more drugs as well as patients above clinically recommended doses, and the means of the average daily doses by drug category., Results: A total of 312,167 patients were identified as a study population. The proportions of patients with three or more drugs in anxiolytics, hypnotics, antidepressants, and antipsychotics significantly decreased after the introduction or revisions of the policy, but not BZs. The proportions of patients with three or more drugs in March 2017 were 0.9%, 2.0%, 1.2%, 2.4%, and 8.9% in anxiolytics, hypnotics, antidepressants, antipsychotics, and BZs, respectively. The effect of the policy in reducing the proportions of patients above clinically recommended doses was identified in antipsychotics after the revision in 2016, but not identified in the sum of anxiolytics and hypnotics as well as BZs after the revision in 2014, and antidepressants after the revision in 2016. The proportions of monotherapy were increased from April 2011 to March 2017 only for antidepressants (76.9% → 80.8%) and antipsychotics (79.8% → 82.1%), and not changed or decreased for anxiolytics (85.2% → 85.7%), hypnotics (78.6% → 77.6%), sum of anxiolytics and hypnotics (68.1% → 65.7%), BZs (68.0% → 67.3%), and sum of psychotropic drugs (52.1% → 49.9%)., Conclusions: The polypharmacy reduction policy reduced the proportions of patients with three or more drugs in four drug categories, but not BZs. Only limited effects were seen for reducing the proportions of patients above clinically recommended doses. The policy was revised in April 2018 again. Further investigation is needed to examine the effect of the revision in 2018.

The Main Association of Austrian Social Security Institutions collects pseudonymized claims data from Austrian social security institutions and information about hospital stays in a database for research purposes. For new studies the same data are repeatedly reprocessed and it is difficult to compare different study results even though the data is already preprocessed and prepared in a proprietary data model. Based on a study on adverse drug events in relation to inappropriate medication in geriatric patients the suitability of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) is analyzed and data is transformed into the OMOP CDM. 1,023 (99.7%) of drug codes and 3,812 (99.2%) of diagnoses codes coincide with the OMOP vocabularies. The biggest obstacles are missing mappings for the Local Vocabularies like the Austrian pharmaceutical registration numbers and the Socio-Economic Index to the OMOP vocabularies. OMOP CDM is a promising approach for the standardization of Austrian claims data. In the long run, the benefits of standardization and reproducibility of research should outweigh this initial drawback.

Purpose: Insurance payers in the United States vary in the indications for which they consider stereotactic body radiation therapy (SBRT) "medically necessary." We compared changes in policies after the last update to the American Society for Radiation Oncology's (ASTRO) SBRT model policy., Methods and Materials: We identified 77 payers with SBRT policies in 2015 from a policy aggregator, as well as 4 national benefits managers (NBMs). Of these, 65 payers and 3 NBMs had publicly available updates since 2015. For each of the indications in ASTRO's model policy, we calculated the proportion of payers that considered SBRT medically necessary. We used Fisher's exact test to compare these proportions between 2015 and now, between policies updated in the past 12 months and those updated less often, and between national and regional payers currently., Results: Payers consider SBRT medically necessary most often for primary lung cancer (97%), reirradiation to the spine (91%), prostate cancer (68%), primary liver cancer (66%), and spinal metastases with radioresistant histologies (66%). Policies have become more aligned with ASTRO's model policy over time. National payers and NBMs cover indications in higher proportions than regional payers., Conclusions: Although there have been improvements over time, more work is needed to align payer policies with ASTRO's model SBRT policy, especially at the regional level., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Managed care organizations are growing more sophisticated in their ability to analyze data. There are increasing numbers of data analysts at managed care organizations, as well as more types of real-time, or "live," data available. These data range from pharmacy claims and enrollment files to medical claims, medical records, and linkages to external data. Moreover, the data are often curated in a way that allows for easier data analysis. Using these data, managed care residents are often required to perform a project to evaluate a utilization management policy or clinical program. Yet, there is a lack of guidance specific to managed care organizations on how to conduct such a research study using "live" claims data. This Viewpoint article provides a primer for managed care residents and other managed care professionals who are seeking to use data to help inform decisions on how to manage their beneficiaries' health and costs. DISCLOSURES: There was no funding source for this manuscript. Hung reports a grant from the Agency for Healthcare Research and Quality and personal fees from Blue Cross Blue Shield Association, outside the submitted work. Gedey, Groeneweg, and Jay have nothing to disclose.

Objectives: Soft-tissue sarcoma (STS) is a heterogeneous group of rare solid tumors that arise from various soft tissues in the body, such as muscle, fat, nerves, and blood vessels. Current International Classification of Diseases (ICD) coding systems include a set of nonspecific codes for malignancies of connective and soft tissue (ICD-9-CM code 171 and ICD-10-CM code C49). The goal of this study was to evaluate the use of these codes for health services research involving patients with a diagnosis of this rare malignancy., Methods: Two databases were utilized to explore ICD coding for STS: claims data from Truven MarketScan and electronic medical records (EMRs) from Flatiron Health. Eligible patients from claims data were those with at least two ICD-9-CM codes of 171.x on two different days between July 1, 2004, and March 30, 2014. The treatment patterns of these cases were evaluated for consistency with known therapeutic approaches for STS. Eligible patients from the Flatiron EMR system were those who received olaratumab (a drug indicated only for use in patients diagnosed with STS) after its US Food and Drug Administration approval in October 2016 through the end of the data set (November 2017). ICD-10-CM codes were evaluated for this known STS cohort., Results: In claims data, 4,159 patients were eligible for inclusion. Although national treatment guidelines include only a limited number of drugs used to treat STS, 98 unique anticancer drugs were identified as being used to treat patients in a claims data cohort. Only 7.7 percent of patients had claims for doxorubicin-based therapy and 3.8 percent had claims for ifosfamide-based therapy as initial treatment for STS, despite these being a standard of care. In the EMR data, 350 patients were eligible; only 170 patients (48.6 percent) had any evidence in the database of a connective or soft-tissue ICD-10-CM malignancy code within 60 days before or after initiation of olaratumab., Conclusions: ICD coding for STS using the "Malignant neoplasm of connective and soft tissue" code is not reliable as a method to identify patients diagnosed with STS. Although codes reflecting the primary site of disease may have clinical relevance, lack of consistency in ICD coding for the diagnosis and treatment of this disease is a limiting factor in the ability to conduct real-world observational research of this rare disease. In the absence of consistent use of this code, an algorithm needs to be developed and validated to accurately identify patients with STS in these databases.

Purpose: The objectives of this study were to investigate sensitivity and specificity of myocardial infarction (MI) case definitions using multiple discharge code positions and multiple diagnosis codes when comparing administrative data to hospital surveillance data., Methods: Hospital surveillance data for ARIC Study cohort participants with matching participant ID and service dates to Centers for Medicare and Medicaid Services (CMS) hospitalization records for hospitalizations occurring between 2001 and 2013 were included in this study. Classification of Definite or Probable MI from ARIC medical record review defined "gold standard" comparison for validation measures. In primary analyses, an MI was defined with ICD9 code 410 from CMS records. Secondary analyses defined MI using code 410 in combination with additional codes., Results: A total of 25 549 hospitalization records met study criteria. In primary analysis, specificity was at least 0.98 for all CMS definitions by discharge code position. Sensitivity ranged from 0.48 for primary position only to 0.63 when definition included any discharge code position. The sensitivity of definitions including codes 410 and 411.1 were higher than sensitivity observed when using code 410 alone. Specificity of these alternate definitions was higher for women (0.98) than for men (0.96)., Conclusion: Algorithms that rely exclusively on primary discharge code position will miss approximately 50% of all MI cases due to low sensitivity of this definition. We recommend defining MI by code 410 in any of first 5 discharge code positions overall and by codes 410 and 411.1 in any of first 3 positions for sensitivity analyses of women., (Copyright © 2018 John Wiley & Sons, Ltd.)

Purpose: To quantify the sensitivity and positive predictive value (PPV) of body mass index (BMI)-related ICD-9-CM and ICD-10-CM diagnosis codes in claims data., Methods: De-identified electronic health record (EHR) and claims data were obtained from the Optum Integrated Claims-Clinical Database for cross-sections of commercial and Medicare Advantage health plan members age ≥ 20 years in 2013, 2014, and 2016. In each calendar year, health plan members' BMI as coded in the insurance claims data (error-prone measure) was compared with their BMI as recorded in the EHR (gold standard) to estimate the sensitivity and PPV of BMI-related ICD-9-CM and ICD-10-CM diagnosis codes. The unit of analysis was the person-year., Results: The study sample included 746 763 distinct health plan members who contributed 1 116 283 eligible person-years (median age 56 years; 57% female; 65% commercially insured and 35% with Medicare Advantage). BMI-related diagnoses were coded for 14.6%. The sensitivity of BMI-related diagnoses codes for the detection of underweight, normal weight, overweight, and obesity was 10.1%, 3.7%, 6.0%, and 25.2%, and the PPV was 49.0% for underweight, 89.6% for normal weight, 73.4% for overweight, and 92.4% for obesity, respectively. The sensitivity of BMI-related diagnosis codes was higher in the ICD-10-CM era relative to the ICD-9-CM era., Conclusions: The PPV of BMI-related diagnosis codes for normal weight, overweight, and obesity was high (>70%) but the sensitivity was low (<30%). BMI-related diagnoses were more likely to be coded in patients with class II or III obesity (BMI ≥35 kg/m 2 ), and in 2016 relative to 2013 or 2014., (© 2018 John Wiley & Sons, Ltd.)

Background: Asthma control is the main focus of treatment guidelines. Valid instruments such as the Asthma Control Questionnaire (ACQ) require prospective survey. These surveys may be challenging for large population health applications., Objective: To develop an algorithm for estimating ACQ-5 scores from commonly available claims data., Methods: Data was derived from four prospective surveys including the ACQ-5 combined with retrospective claims of Kaiser Permanente of Colorado (KPCO) patients. The statistical approach consisted of derivation and validation of a prediction algorithm including medical and pharmacy claims data using stepwise regression elimination. Validation was conducted by estimating mean squared error (MSE) and mean absolute error (MAE) in one hundred split-sample iterations. Ordinary least squares (OLS), Tobit and Median regression were used., Results: There were 2,657 individuals with valid ACQ-5 scores, claims and eligibility at baseline. The following had statistically significant associations with ACQ-5 scores: gender, use of oral corticosteroids and short-acting beta agonists, the number of asthma drug classes, and emergency and outpatient visits. Average MSE and MAE were similar for the estimation and validation samples., Conclusion: This research provides preliminary results of the feasibility of predicting ACQ-5 scores using commonly available medical and pharmacy claims data. The resulting algorithm may facilitate public health and population level analyses of asthma control. Future studies in different populations will be important to validate the algorithm.

Objective: To investigate magnitude and sources of discrepancy in quality metrics using claims versus electronic health record (EHR) data., Study Design: Assessment of proportions of HbA1c and LDL testing for people ascertained as diabetic from the respective sources. Qualitative interviews and review of EHRs of discrepant cases., Data Collection/extraction: Claims submitted to Rhode Island Medicaid by three practice sites in 2013; program-coded EHR extraction; manual review of selected EHRs., Principal Findings: Of 21,030 adult Medicaid beneficiaries attributed to a primary care patient at a site by claims or EHR data, concordance on assignment ranged from 0.30 to 0.41. Of patients with concordant assignment, the ratio of patients ascertained as diabetic by EHR versus claims ranged from 1.06 to 1.14. For patients with concordant assignment and diagnosis, the ratio based on EHR versus claims ranged from 1.08 to 18.34 for HbA1c testing, and from 1.29 to 14.18 for lipid testing. Manual record review of 264 patients discrepant on diagnosis or testing identified problems such as misuse of ICD-9 codes, failure to submit claims, and others., Conclusions: Claims data underestimate performance on these metrics compared to EHR documentation, by varying amounts. Use of claims data for these metrics is problematic., (© Health Research and Educational Trust.)

Electronic health records may have digitized patient data, but getting that data from one clinician to another remains a huge challenge, especially since patients often have multiple doctors ordering tests, prescribing drugs, and providing treatment. Many experts now believe that blockchain technology might be just the thing to get a patient's pertinent medical information from where it is stored to where it is needed, as well as to allow patients to easily view their own medical histories. In addition, blockchain technology might also be able to help with other aspects of health care, such as improving the insurance claim or other administrative processes within healthcare networks and making health-related population data available to biomedical researchers.

Background: The "meaningful use of certified electronic health record" policy requires eligible professionals to record smoking status for more than 50% of all individuals aged 13 years or older in 2011 to 2012., Objectives: To explore whether the coding to document smoking behavior has increased over time and to assess the accuracy of smoking-related diagnosis and procedure codes in identifying previous and current smokers., Methods: We conducted an observational study with 5,423,880 enrollees from the year 2009 to 2014 in the Truven Health Analytics database. Temporal trends of smoking coding, sensitivity, specificity, positive predictive value, and negative predictive value were measured., Results: The rate of coding of smoking behavior improved significantly by the end of the study period. The proportion of patients in the claims data recorded as current smokers increased 2.3-fold and the proportion of patients recorded as previous smokers increased 4-fold during the 6-year period. The sensitivity of each International Classification of Diseases, Ninth Revision, Clinical Modification code was generally less than 10%. The diagnosis code of tobacco use disorder (305.1X) was the most sensitive code (9.3%) for identifying smokers. The specificities of these codes and the Current Procedural Terminology codes were all more than 98%., Conclusions: A large improvement in the coding of current and previous smoking behavior has occurred since the inception of the meaningful use policy. Nevertheless, the use of diagnosis and procedure codes to identify smoking behavior in administrative data is still unreliable. This suggests that quality improvements toward medical coding on smoking behavior are needed to enhance the capability of claims data for smoking-related outcomes research., (Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate the performance of delirium-identification algorithms in administrative claims and drug utilization data., Methods: We used data from a prospective study of 184 older adults who underwent aortic valve replacement at a single academic medical center to evaluate the following delirium-identification algorithms: (1) International Classification of Diseases (ICD) diagnosis codes for delirium; (2) antipsychotics use; (3) either ICD diagnosis codes or antipsychotics use; and (4) both ICD diagnosis codes and antipsychotics use. These algorithms were evaluated against a validated bedside assessment, the Confusion Assessment Method, and a validated delirium severity scale, the CAM-S., Results: Delirium occurred in 66 patients (36%), of which 14 (21%) had hyperactive or mixed features and 15 (23%) had severe delirium. ICD diagnosis codes for delirium were present in 15 patients (8%). Antipsychotics were used in 13 patients (7%). ICD diagnosis codes alone and antipsychotics use alone had comparable sensitivity (18% vs. 18%) and specificity (98% vs. 99%). Defining delirium using either ICD diagnosis codes or antipsychotics use, sensitivity improved to 30% with little change in specificity (97%). This algorithm showed higher sensitivity for hyperactive or mixed delirium (64%) and severe delirium (73%). Requiring both ICD diagnosis codes and antipsychotics use resulted in perfect specificity but low sensitivity (6%)., Conclusion: Delirium-identification algorithms in claims data have low sensitivity and high specificity. Defining delirium using ICD diagnosis codes or antipsychotics use performs better than considering either type of information alone. This information should inform the design and interpretation of claims-based comparative effectiveness and safety research. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Objective: Outcome misclassification may occur in observational studies using administrative databases. We evaluated a two-step multiple imputation approach based on complementary internal validation data obtained from two subsamples of study participants to reduce bias in hazard ratio (HR) estimates in Cox regressions., Methods: We illustrated this approach using data from a surveyed sample of 6247 individuals in a study of statin-diabetes association in Quebec. We corrected diabetes status and onset assessed from health administrative data against self-reported diabetes and/or elevated fasting blood glucose (FBG) assessed in subsamples. The association between statin use and new onset diabetes was evaluated using administrative data and the corrected data. By simulation, we assessed the performance of this method varying the true HR, sensitivity, specificity, and the size of validation subsamples., Results: The adjusted HR of new onset diabetes among statin users versus non-users was 1.61 (95% confidence interval: 1.09-2.38) using administrative data only, 1.49 (0.95-2.34) when diabetes status and onset were corrected based on self-report and undiagnosed diabetes (FBG ≥ 7 mmol/L), and 1.36 (0.92-2.01) when corrected for self-report and undiagnosed diabetes/impaired FBG (≥ 6 mmol/L). In simulations, the multiple imputation approach yielded less biased HR estimates and appropriate coverage for both non-differential and differential misclassification. Large variations in the corrected HR estimates were observed using validation subsamples with low participation proportion. The bias correction was sometimes outweighed by the uncertainty introduced by the unknown time of event occurrence., Conclusion: Multiple imputation is useful to correct for outcome misclassification in time-to-event analyses if complementary validation data are available from subsamples. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

The In-hospital Mortality for PulmonAry embolism using Claims daTa (IMPACT) rule can accurately identify pulmonary embolism (PE) patients at low risk of early complications using claims data. We sought to externally validate the IMPACT and simplified Pulmonary Embolism Severity Index (sPESI) tools for predicting all-cause mortality and readmission. We used Veteran Health Administration data (10/1/2010-9/30/2015) to identify adults with ≥1 inpatient diagnosis code for acute PE, ≥12 months continuous medical and pharmacy benefits prior to the index PE, ≥90 days of post-event follow-up (unless death occurred) and ≥1 claim for an anticoagulant during the index PE stay. Prognostic accuracies of IMPACT and sPESI for 30- and 90-day all-cause mortality and 90-day readmission were estimated. Of 6,746 PE patients, 7.5 and 12.6% died at 30 and 90 days. Within 90 days, 20.1% were readmitted for any reason. Hospitalization for recurrent VTE and major bleeding occurred in 5.6 and 1.7% of patients. IMPACT classified 15.2% as low risk, while 28.4% were low risk per sPESI. Both tools displayed sensitivity >90% and negative predictive values (NPVs) >97% for 30-day mortality, but low specificity (range 16.2-30.0) and positive predictive values (PPVs) (range 8.7-9.5); with similar results observed for 90-day mortality. IMPACT's sensitivity for all-cause readmission was numerically higher than sPESI (88.2 vs. 79.0%), but both had comparable NPVs (85.1 vs. 84.2%). Similar trends were observed for VTE or major bleeding readmissions. IMPACT classified patients for post-PE outcomes with similar accuracy as sPESI. IMPACT appears useful for identifying PE patients at low risk for early mortality or readmission in claims-based studies.

Background: Granular clinical and laboratory data available in electronic health record (EHR) databases provide researchers the opportunity to conduct investigations that would not be possible in insurance claims databases; however, for pharmacoepidemiology studies, accurate classification of medication exposure is critical., Objective: The aim of this study was to evaluate the validity of classifying medication exposure using EHR prescribing (EHR-Rx) data., Methods: We conducted a retrospective cohort study among patients with linked claims and EHR data in OptumLabs™ Data Warehouse. The agreement between EHR-Rx data and pharmacy claims (PC-Rx) data (for 40 medications) was determined using the positive predictive value (PPV) and medication possession ratio (MPR)-calculated in 1- and 12-month medication exposure periods (MEPs). Secondary analyses were restricted to incident vs prevalent EHR-Rxs, age ≥65 vs <65, white vs black race, males vs females, and number of EHR-Rxs., Results: The validity metrics varied substantially among the 40 medications assessed. Across all medications, the period PPV and MPR were 62% and 63% in the 1-month MEP. They were 78% and 43% in the 12-month MEP. Overall, PPV and MPR were higher for patients with a prevalent EHR-Rx and age <65., Conclusions: Despite substantial variability among different medications, there was very good agreement between EHR-Rx data and PC-Rx data. To maximize the validity of classifying medication exposure with EHR prescribing data, researchers may consider using longer MEPs (eg, 12 months) and potentially require multiple EHR-Rxs to classify baseline medication exposure., (Copyright © 2017 John Wiley & Sons, Ltd.)

Background: The Medicare star ratings system incentivizes health plan sponsors based on their performance across a measurement system that contains quality measures related to medication use. As health plan sponsors seek to further engage their network providers, specifically network pharmacies, to improve performance on these measures, it is important to consider the effect of environmental factors on the performance of network pharmacies., Objective: To determine the effect of environmental factors on pharmacy quality as measured by (a) medication adherence for noninsulin diabetes medications, (b) medication adherence for renin angiotensin receptor antagonists (RASA), (c) medication adherence for cholesterol medications (statins), and (d) use of high-risk medications (HRM) in the elderly., Methods: The EQuIPP database, which contains performance information for pharmacies for a nationwide sample of Medicare beneficiaries, was used for this analysis. Environmental factors included regions or characteristics of a community or county. County-level data was obtained from the Area Health Resource Files, a resource product available from the Health Resources & Service Administration. A logistic regression model was developed with performance as the dependent variable and regions and environmental factors as independent variables. Performance and county characteristics, such as proportion of patients in an age group, race, income, or number of outpatient visits, were classified as high and low based on a median cutoff of nationwide performance scores., Results: A total of 28,950 pharmacies were included in this analysis. For most measures, the proportion of low-performing pharmacies was significantly higher in the East South Central, Mid-Atlantic, Mountain, Pacific, and West South Central regions. Pharmacies in counties with high median income, high proportion of elderly population (aged > 84 years), high proportion of elderly patients who were white or Hispanic, high proportion of elderly males, and high proportion of elderly urban patients were less likely to have low performance, whereas those with high proportion of elderly African Americans and high density of independent pharmacies were more likely to have low performance (P < 0.05-0.0001)., Conclusions: This study found that environmental characteristics of a region, including pharmacy and sociodemographic characteristics, explained regional variation in quality measures related to medication use for patient populations served by pharmacies. This evaluation serves to further inform the discussion regarding case-mix adjustment of quality measures and provides information that may be important to further refine intervention strategies for pharmacies and pharmacists who serve certain regional populations. Additionally, pharmacies in greatest need of support for quality improvement may be those who serve populations that are predominantly low income and elderly African American., Disclosures: Desai's postdoctoral fellowship was funded by Pharmacy Quality Solutions for conducting this study and writing the manuscript. Nau and Conklin are employed by Pharmacy Quality Solutions. An earlier version of this research was presented as a poster at the Annual Meeting of the Academy of Managed Care Pharmacy; San Diego, CA; April 7-10, 2015. Study concept and design were contributed by Conklin, Nau, Desai, and Heaton. Desai and Conklin took the lead in data collection, assisted by Nau and Heaton. The manuscript was primarily written by Desai and Heaton, with assistance from Conklin and Nau. All authors contributed to data interpretation and manuscript revision.

Background: External quality assurance for revisions of total knee arthroplasty (TKA) and total hip arthroplasty (THA) are carried out through the AQUA institute in Germany. Data are collected by the providers and are analyzed based on predefined quality indicators from the hospital stay in which the revision was performed. The present study explores the possibility to add routine data analysis to the existing external quality assurance (EQS). Differences between methods are displayed. The study aims to quantify the benefit of an additional analysis that allows patients to be followed up beyond the hospitalization itself., Material and Methods: All persons insured in an AOK sickness fund formed the population for analysis. Revisions were identified using the same algorithm as the existing external quality assurance. Adverse events were defined according to the AQUA indicators for the years 2008 to 2011.The hospital stay in which the revision took place and a follow-up of 30 days were included. For re-operation and dislocation we also defined a 365 days interval for additional follow-up. The results were compared to the external quality control reports., Results: Almost all indicators showed higher events in claims data analysis than in external quality control. Major differences are seen for dislocation (EQS SD: 1.87 vs. claims data [cd] SD: 2.06 %, cd+30 d: 2.91 %, cd+365 d: 7.27 %) and reoperation (hip revision: EQS SD: 5.88 % vs. claims data SD: 8.79 % cd+30 d: 9.82 %, cd+365 d: 15.0 %/knee revision: EQS SD: 3.21 % vs. claims data SD: 4.07 %, cd+30 d: 4.6 %, cd+365 d: 15.43 %). Claims data could show additional adverse events for all indicators after the initial hospital stay, rising to 77 % of all events., Conclusions: The number of adverse events differs between the existing external quality control and our claims data analysis. Claims data give the opportunity to complement existing methods of quality control though a longer follow-up, when many complications become evident., (Georg Thieme Verlag KG Stuttgart · New York.)

Objective: The state of opioid consumption among cancer patients has never been comprehensively investigated in Japan. The Diagnosis Procedure Combination claims data may be used to measure and monitor opioid consumption among cancer patients, but the accuracy of using the Diagnosis Procedure Combination data for this purpose has never been tested., Methods: We aimed to ascertain the accuracy of using the Diagnosis Procedure Combination claims data for estimating total opioid analgesic consumption by cancer patients compared with electronic medical records at Aomori Prefectural Central Hospital. We calculated percent differences between estimates obtained from electronic medical records and Diagnosis Procedure Combination claims data by month and drug type (morphine, oxycodone, fentanyl, buprenorphine, codeine and tramadol) between 1 October 2012 and 30 September 2013, and further examined the causes of discrepancy by reviewing medical and administrative charts between April and July 2013., Results: Percent differences varied by month for drug types with small prescription volumes, but less so for drugs with larger prescription volumes. Differences also tended to diminish when consumption was compared for a year instead of a month. Total percent difference between electronic medical records and Diagnosis Procedure Combination data during the study period was -0.1% (4721 mg per year per hospital), as electronic medical records as baseline. Half of the discrepancy was caused by errors in data entry., Conclusion: Our study showed that Diagnosis Procedure Combination claims data can be used to accurately estimate opioid consumption among a population of cancer patients, although the same conclusion cannot be made for individual estimates or when making estimates for a group of patients over a short period of time., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: In nonexperimental comparative effectiveness research using health care databases, outcome measurements must be validated to evaluate and potentially adjust for misclassification bias. We aimed to validate claims-based myocardial infarction (MI) algorithms in a Medicaid population using an HIV clinical cohort as the gold standard., Methods: Medicaid administrative data were obtained for the years 2002-2008 and linked to the UNC CFAR HIV Clinical Cohort based on social security number, first name, and last name and MI were adjudicated. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated., Results: There were 1063 individuals included in the study. Over a median observed time of 2.5 years, 17 had an MI. Specificity ranged from 0.979 to 0.993 with the highest specificity obtained using the ICD-9 code 410.xx in the primary or secondary position and a length of stay >3 days. Sensitivity of MI ascertainment varied from 0.588 to 0.824 depending on algorithm., Conclusions: Specificities of varying claims-based MI ascertainment criteria are high but small changes impact positive predictive value in a cohort with low incidence. Sensitivities vary based on ascertainment criteria. Type of algorithm used should be prioritized based on study question and maximization of specific validation parameters that will minimize bias while also considering precision.

We describe the results of a survey of claim forms that are used when starting rehabilitation following inpatient treatment and of an evaluation of a claim form developed on the basis of the results. The survey of different existing forms shows a high overlapping in content, suggesting the possibility of unification to one claim form that can be accepted by all insurers. In analogy to the Delphi method criteria for evaluation were consented and applied by the author group to assess the relevance of the claim forms content items for the process of initiating rehabilitation. A group of further experts added their evaluations. We prioritised the results and extracted the essential contents to conceive a unified claim form eligible for all types of rehabilitation. The claim form was discussed in 3 focus groups, revised accordingly and tested in the Hannover Medical School. Test results show that all relevant information is asked for and that the form is well manageable. The users' request for an IT-based solution and further ideas for improvement were integrated into the revised and validated version of the claim form. It is now available for all stake holders, in particular for insurers, as a means to improve quality of care and efficiency by standardisation of rehabilitation claim forms., (© Georg Thieme Verlag KG Stuttgart · New York.)

Purpose: To examine the validity of claims data to identify colorectal cancer (CRC) recurrence and determine the extent to which misclassification of recurrence status affects estimates of its association with overall survival in a population-based administrative database., Methods: We calculated the accuracy of claims data relative to medical records from one large tertiary hospital to identify CRC recurrence. We estimated the effect of misclassifying recurrence on survival by applying these findings to the linked Surveillance, Epidemiology, and End Results-Medicare data., Results: Of 174 eligible CRC patients identified through medical records, 32 (18.4%) had a recurrence. A claims-based algorithm of secondary malignancy codes yielded a sensitivity of 81% and specificity of 99% for identifying recurrence. Agreement between data sources was almost perfect (kappa: 0.86). In a model unadjusted for misclassification, CRC patients with recurrence were 3.04 times (95% confidence interval: 2.92-3.17) more likely to die of any cause than those without recurrence. In the corrected model, CRC patients with recurrence were 3.47 times (95% confidence interval: 3.06-4.14) more likely to die than those without recurrence., Conclusions: Identifying recurrence in CRC patients using claims data is feasible with moderate sensitivity and high specificity. Future studies can use this algorithm with Surveillance, Epidemiology, and End Results-Medicare data to study treatment patterns and outcomes of CRC patients with recurrence., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Use of administrative claims from multiple sources for research purposes is challenged by the lack of consistency in the structure of the underlying data and definition of data across claims data providers. This paper evaluates the impact of applying a standardized revenue code-based logic for defining inpatient encounters across two different claims databases., Methods: We selected members who had complete enrollment in 2012 from the Truven MarketScan Commercial Claims and Encounters (CCAE) and the Optum Clinformatics (Optum) databases. The overall prevalence of inpatient conditions in the raw data was compared to that in the common data model (CDM) with the standardized visit definition applied., Results: In CCAE, 87.18% of claims from 2012 that were classified as part of inpatient visits in the raw data were also classified as part of inpatient visits after the data were standardized to CDM, and this overlap was consistent from 2006 to 2011. In contrast, Optum had 83.18% concordance in classification of 2012 claims from inpatient encounters before and after standardization, but the consistency varied over time. The re-classification of inpatient encounters substantially impacted the observed prevalence of medical conditions occurring in the inpatient setting and the consistency in prevalence estimates between the databases. On average, before standardization, each condition in Optum was 12% more prevalent than that same condition in CCAE; after standardization, the prevalence of conditions had a mean difference of only 1% between databases. Amongst 7,039 conditions reviewed, the difference in the prevalence of 67% of conditions in these two databases was reduced after standardization., Conclusions: In an effort to improve consistency in research results across database one should review sources of database heterogeneity, such as the way data holders process raw claims data. Our study showed that applying the Observational Medical Outcomes Partnership (OMOP) CDM with a standardized approach for defining inpatient visits during the extract, transfer, and load process can decrease the heterogeneity observed in disease prevalence estimates across two different claims data sources.

Background: Duplicate prescribing is known to occur across health systems and is one of the most frequent drug related problems. Therapeutic duplication (TD) increases the risk of adverse drug reactions without additional therapeutic benefits., Objectives: This study aimed to develop TD criteria concerning four drug categories which are acid-related disorder drugs, antimicrobials, antihypertensives, and lipid modifying drugs and to estimate the prevalence of therapeutic duplicate prescribing at the ambulatory care settings in Korea., Methods: TD criteria were developed using the WHO anatomical therapeutic chemical classification and modified with an expert consensus panel using the Delphi method. The prevalence of TD including ingredient duplication (ID) of four drug categories was examined using National Health Insurance claim database including 15 million patients during one month in 2009 (December). TD was defined as prescribing medications within the same category in the developed TD criteria list., Results: The numbers of patients who received acid-related disorder drugs, antimicrobials, antihypertensives, and lipid-modifying drugs in the study period were 10,049,292, 7,584,131, 4,349,945, and 1,425,292 respectively. In the field of acid-related disorder drugs prescribed, there were 0.3 % IDs and 2.5 % TDs within a prescription issued by one prescriber. There were 8.4 % IDs and 14.5 % TDs between prescriptions issued at different ambulatory visits. In the field of antimicrobial medicines, there were 0.1 % IDs and 2.6 % TDs within a prescription, while there were 5.0 % IDs and 7.6 % TDs between different prescriptions. Amongst the antihypertensives prescribed, there were 0.4 % IDs and 1.9 % TDs within a prescription, while there were 9.9 % IDs and 11.5 % TDs between prescriptions. Lastly, looking at lipid-modifying drugs prescribed, there were 0.3 % IDs and 0.5 % TDs within one prescription, while there were 8.9 % IDs and 9.4 % TDs between prescriptions., Conclusion: The prevalence of duplicate prescribing was substantial in the ambulatory care setting which is to be improved using the TD criteria developed from this study in the national drug utilization review system in Korea.

Reports on the quality of care aim at health and patient-reported outcomes in routine clinical care. To achieve meaningful information the study designs must be robust against bias through highly selected patient populations or health care providers but also allow for adequate control of confounding. The article describes the potential and pitfalls of administrative claims data and surveys of beneficiaries. The large potential of using both sources is illustrated in the primary inpatient treatment for prostate cancer. However, linking claims data and patient survey data still leaves some problems to be addressed in the final section. Linking claims data and beneficiary survey information on patient reported outcomes overcomes sectoral barriers and allows for an integrated evaluation of pathways of care in the short-, mid- and long-term. It is economical and well suited for a variety, but not all health care problems. Future efforts might be directed towards more collaboration among sickness funds., (© Georg Thieme Verlag KG Stuttgart · New York.)

Purpose: The development and successful implementation of a comprehensive medication prior-authorization service are described., Summary: At our 550-bed academic medical center, we found an increasing number of Medicare write-offs as a result of the off-label use of specialty medications. Furthermore, more insurers were requiring that prior authorization be attained before patients began treatment with a high-cost specialty medication. After investigating who was pursuing these prior authorizations and where they were documented, it became clear that no department within the hospital owned this responsibility. Our pharmacy department initiated a specialty medication prior-authorization and medication assistance program to review all medication orders written for high-cost specialty medications administered or infused in any of the health system's outpatient clinics, including chemotherapy infusions. A full-time specialty pharmacy coordinator was hired in fall 2010. This pharmacist was responsible for spearheading the overall specialty medication strategy for the health system. Our prior-authorization work across our health system has not only helped to reduce Medicare write-offs and commercial insurance denials but also has resulted in closer working relationships with individuals in other departments of the hospital. Several million dollars in additional write-offs were avoided because our prior-authorization staff identified prescribed therapies that would not be covered by Medicare or commercial insurers before they were started. In total, we decreased write-offs or increased revenues by over $6.2 million during this time frame, a return on investment exceeding 20:1., Conclusion: A comprehensive medication prior-authorization service generated additional revenue due to a decrease in lost charges and an increase in net reimbursement., (Copyright © 2015 by the American Society of Health-System Pharmacists, Inc. All rights reserved.)

Purpose: Days supply (prescription duration) values are commonly used to estimate drug exposure and quantify adherence to therapy, yet accuracy is not routinely assessed, and potential inaccurate reporting has been previously identified. We examined the impact of cleaning days supply values on the measurement of adherence to oral bisphosphonates., Methods: We identified new users of oral bisphosphonates among Ontario seniors (April 2001-March 2011). Days supply values were examined by dose, and we identified misclassification by comparing observed values to dose-specific expected values. Days supply values not matching expected values were cleaned using dose-specific algorithms. One-year adherence to therapy was defined using measures of compliance (mean proportion of days covered [PDC], and categorized into high [PDC ≥ 80%], medium [50% < PDC < 80%], low [PDC ≤ 50%]) and persistence (30-day permissible gap). Estimates were compared using the observed and cleaned days supply values, stratified by site of patient residence (community or long-term care [LTC])., Results: We identified 337 729 (5% LTC) eligible new users. Among LTC patients, adherence estimates increased significantly following data cleaning: mean PDC (59 to 83%), proportion with high compliance (47 to 76%), and proportion persisting with therapy (62 to 78%). Modest increases were identified among community-dwelling patients following data cleaning (mean PDC, 71 to 74%; high compliance, 54 to 58%; and persistence, 56 to 61%)., Conclusions: Data cleaning to correct for exposure misclassification can influence estimates of adherence with oral bisphosphonate therapy, particularly in LTC. Results highlight the importance of developing data cleaning strategies to correct for exposure misclassification and improve transparency in pharmacoepidemiologic studies., (Copyright © 2014 John Wiley & Sons, Ltd.)

Big Data in the healthcare setting refers to the storage, assimilation, and analysis of large quantities of information regarding patient care. These data can be collected and stored in a wide variety of ways including electronic medical records collected at the patient bedside, or through medical records that are coded and passed to insurance companies for reimbursement. When these data are processed it is possible to validate claims as a part of the regulatory review process regarding the anticipated performance of medications and devices. In order to analyze properly claims by manufacturers and others, there is a need to express claims in terms that are testable in a timeframe that is useful and meaningful to formulary committees. Claims for the comparative benefits and costs, including budget impact, of products and devices need to be expressed in measurable terms, ideally in the context of submission or validation protocols. Claims should be either consistent with accessible Big Data or able to support observational studies where Big Data identifies target populations. Protocols should identify, in disaggregated terms, key variables that would lead to direct or proxy validation. Once these variables are identified, Big Data can be used to query massive quantities of data in the validation process. Research can be passive or active in nature. Passive, where the data are collected retrospectively; active where the researcher is prospectively looking for indicators of co-morbid conditions, side-effects or adverse events, testing these indicators to determine if claims are within desired ranges set forth by the manufacturer. Additionally, Big Data can be used to assess the effectiveness of therapy through health insurance records. This, for example, could indicate that disease or co-morbid conditions cease to be treated. Understanding the basic strengths and weaknesses of Big Data in the claim validation process provides a glimpse of the value that this research can provide to industry. Big Data can support a research agenda that focuses on the process of claims validation to support formulary submissions as well as inputs to ongoing disease area and therapeutic class reviews.

Opioids are the mainstay of treatment for moderate to severe cancer pain. The variations in average monthly cost can make it difficult for most patients to procure them without adequate insurance coverage. There are increasing numbers of denials of payment and statements made by insurance agents and other sources regarding inappropriate opioid use, resulting in severe pain and emotional distress for cancer patients and their families. This case series describes five events where the insurer was a major barrier to opioid access.

Background: This study compares registry and non-registry approaches to linking 2006 Census of Population data for Manitoba and Ontario to hospital data from the Discharge Abstract Database (DAD)., Data and Methods: Using a probabilistic linkage, the registry approach linked the census data to provincial health insurance registries, followed by a deterministic linkage to the DAD based on health insurance number (HIN). The non-registry approach used hierarchical deterministic exact matching based on three variables common to both files to link census data to the DAD. The approaches were compared in terms of linkage and coverage rates, sensitivity and specificity, and consistency of HINs on the linked records., Results: Results of the registry and non-registry linkage approaches were similar. In Manitoba, 7% and 6% of census long-form respondents linked to the DAD with the registry and non-registry linkage approaches, respectively; in Ontario, the linkage rate was 5% for both approaches. With the registry approach, the linked census-DAD data represented 84% (weighted) of hospital admissions in the 2006/2007 DAD in both provinces, compared with 82% in Manitoba and Ontario with the non-registry approach., Interpretation: In the absence of access to provincial health insurance registries with which census data can be linked, a non-registry approach can be used to create a research-quality dataset.

Purpose: Researchers are using diagnosis codes from health claims to identify metastatic disease in cancer patients. The validity of this approach has not been established., Methods: We used the linked 2005-2007 Surveillance, Epidemiology and End Results (SEER)-Medicare data to assess the validity of metastasis codes at diagnosis from claims compared with stage reported by SEER cancer registries. The cohort included 80,052 incident breast, lung, and colorectal cancer patients aged 65 years and older. Using gold-standard SEER data, we evaluated sensitivity, specificity, positive predictive value, and negative predictive value of claims-based stage, survival by stage classification, and patient factors associated with stage misclassification using multivariable regression., Results: For patients with a registry report of distant metastatic cancer, the sensitivity, specificity, and positive predictive value of claims never simultaneously exceeded 80% for any cancer: lung (42.7%, 94.8%, and 88.1%), breast (51.0%, 98.3%, and 65.8%), and colorectal (72.8%, 93.8%, and 68.5%). Misclassification of stage from Medicare claims was significantly associated with inaccurate estimates of stage-specific survival (P < .001). In adjusted analysis, patients who were older, black, or living in low-income areas were more likely to have their stage misclassified in claims., Conclusions: Diagnosis codes in Medicare claims have limited validity for inferring cancer stage and metastatic disease., (Published by Elsevier Inc.)