Processing and validation of inpatient Medicare Advantage data for use in hospital outcome measures

IF 3.1 2区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Kelly A. Kyanko MD, MHS, Kashika M. Sahay PhD, MPH, Yongfei Wang MS, Michelle Schreiber MD, Melissa Hager MSN, BSN, RN, Raquel Myers PhD, JD, MPH, Wanda Johnson BS, RN, Jing Zhang MBA, MPhil, MS, Bing-Jie Yen MPH, MA, Lisa G. Suter MD, Elizabeth W. Triche PhD, Shu-Xia Li PhD
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引用次数: 0

Abstract

Objective

To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter- and hospital-submitted inpatient claims.

Data Sources and Study Setting

Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges.

Study Design

We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim.

Data Collection/Extraction Methods

We combined hospital- and MAO-submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO-submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures.

Principal Findings

After removing void and duplicate claims, identifying overlapped claims between the hospital- and MAO-submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital-submitted claims were submitted within 3 months after a one-year performance period, versus 85.2% of MAO-submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code.

Conclusions

Inpatient MA data are feasible for use in CMS claims-based hospital outcome measures. We recommend prioritizing hospital-submitted over MAO-submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors.

处理和验证住院病人医疗保险优势数据,以用于医院结果测量。
目标:通过结合医疗保险优势组织(MAO)的遭遇和医院提交的住院病人索赔,确定将医疗保险优势组织(MA)的入院情况纳入医疗保险与医疗补助服务中心(CMS)医院结果测量的可行性:研究设计:我们检查了MA报销单的及时性,比较了由医院和MAO同时提交报销单(重叠报销单)的入院诊断和程序代码,并比较了重叠报销单入院与单一报销单入院的人口统计特征和主要诊断代码:我们将医院和 MAO 提交的索赔合并,以获取所有医院的 MA 住院病例,并确定重叠索赔。对于仅有 MAO 提交报销单的入院患者,我们使用医疗服务提供者历史数据将报销单上的全国医疗服务提供者标识符与 CMS 结果测量报告中使用的 CMS 认证号进行匹配:在剔除无效和重复索赔、识别医院和 MAO 提交的数据集之间的重叠索赔、将索赔限制在急症护理和危重病医院以及捆绑相同入院索赔后,我们确定了 5,078,611 例 MA 入院病人。其中,76.1% 由医院和 MAO 同时提交,14.2% 仅由 MAO 提交,9.7% 仅由医院提交。几乎所有(96.6%)由医院提交的报销申请都是在一年绩效期后的 3 个月内提交的,而由 MAO 提交的报销申请则为 85.2%。在 3,864,524 份索赔重叠的住院病例中,98.9% 的病例在两个数据集中共享相同的主要诊断代码,97.5% 的病例共享相同的第一个手术代码:住院医疗管理数据可用于基于 CMS 索赔的医院结果测量。我们建议在进行分析时优先考虑医院提交的索赔,而不是 MAO 提交的索赔。监测、数据审计和改善医疗保险数据质量的持续政策是解决潜在数据缺失和错误的重要方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Health Services Research
Health Services Research 医学-卫生保健
CiteScore
4.80
自引率
5.90%
发文量
193
审稿时长
4-8 weeks
期刊介绍: Health Services Research (HSR) is a peer-reviewed scholarly journal that provides researchers and public and private policymakers with the latest research findings, methods, and concepts related to the financing, organization, delivery, evaluation, and outcomes of health services. Rated as one of the top journals in the fields of health policy and services and health care administration, HSR publishes outstanding articles reporting the findings of original investigations that expand knowledge and understanding of the wide-ranging field of health care and that will help to improve the health of individuals and communities.
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