The Volume and Cost of Quality Metric Reporting.

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.

Health Care Resource Utilization and Costs Among Medicare Beneficiaries Newly Diagnosed With Peripheral T-cell Lymphoma: A Retrospective Claims Analysis.

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.

Evaluation of claims-based computable phenotypes to identify heart failure patients with preserved ejection fraction.

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.

A Call to Action to Track Generic Drug Quality Using Real-World Data and the FDA's Sentinel Initiative.

DISCLOSURES: The author reports funding from the U.S. Food and Drug Administration to study real-world data approaches to detect generic drug quality issues. There are no further conflicts or disclosures.

Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims.

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.

Characterizing female patients with haemophilia A: Administrative claims analysis and medical chart review.

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.

Translating claims-based CHA2 DS2 -VaSc and HAS-BLED to ICD-10-CM: Impacts of mapping strategies.

PURPOSE: The CHA2 DS2 -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 CHA2 DS2 -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 CHA2 DS2 -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 CHA2 DS2 -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 CHA2 DS2 -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 CHA2 DS2 -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 CHA2 DS2 -VaSc; 0.719 [0.702-0.737] for HAS-BLED) or direct OACs (0.695 [0.654-0.735] for CHA2 DS2 -VaSc; 0.700 [0.673-0.728] for HAS-BLED) than those taking warfarin (0.655 [0.613-0.697] for CHA2 DS2 -VaSc; 0.663 [0.6320.695] for HAS-BLED). CONCLUSIONS: Existing mapping strategies generally preserved the distributions of CHA2 DS2 -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.

Validation of claims-based algorithms for psoriatic arthritis.

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.

Evaluating the Utility of Coarsened Exact Matching for Pharmacoepidemiology Using Real and Simulated Claims Data.

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.

Feasibility of Mapping Austrian Health Claims Data to the OMOP Common Data Model.

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.

Changes in Prescription of Psychotropic Drugs After Introduction of Polypharmacy Reduction Policy in Japan Based on a Large-Scale Claims Database.

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.

A Primer for Managed Care Residents: How to Conduct Research Using Live Medical and Pharmacy Claims Data.

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.

Challenges of Using ICD-9-CM and ICD-10-CM Codes for Soft-Tissue Sarcoma in Databases for Health Services Research.

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.

Differences in United States Insurance Payer Policies and American Society for Radiation Oncology's (ASTRO) Model Policy on Stereotactic Body Radiation Therapy (SBRT).

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.

Validation of body mass index (BMI)-related ICD-9-CM and ICD-10-CM administrative diagnosis codes recorded in US claims data.

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/m2 ), and in 2016 relative to 2013 or 2014.

(Block) Chain Reaction: A Blockchain Revolution Sweeps into Health Care, Offering the Possibility for a Much-Needed Data Solution.

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.