Suppr超能文献

估算为精神分裂症患者的提供者提供更准确药物依从性信息的新技术的价值。

Estimating the Value of New Technologies That Provide More Accurate Drug Adherence Information to Providers for Their Patients with Schizophrenia.

机构信息

1 Precision Health Economics, Los Angeles, California.

2 Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles.

出版信息

J Manag Care Spec Pharm. 2016 Nov;22(11):1285-1291. doi: 10.18553/jmcp.2016.22.11.1285.

Abstract

BACKGROUND

Nonadherence to antipsychotic medication among patients with schizophrenia results in poor symptom management and increased health care and other costs. Despite its health impact, medication adherence remains difficult to accurately assess. New technologies offer the possibility of real-time patient monitoring data on adherence, which may in turn improve clinical decision making. However, the economic benefit of accurate patient drug adherence information (PDAI) has yet to be evaluated.

OBJECTIVE

To quantify how more accurate PDAI can generate value to payers by improving health care provider decision making in the treatment of patients with schizophrenia.

METHODS

A 3-step decision tree modeling framework was used to measure the effect of PDAI on annual costs (2016 U.S. dollars) for patients with schizophrenia who initiated therapy with an atypical antipsychotic. The first step classified patients using 3 attributes: adherence to antipsychotic medication, medication tolerance, and response to therapy conditional on medication adherence. The prevalence of each characteristic was determined from claims database analysis and literature reviews. The second step modeled the effect of PDAI on provider treatment decisions based on health care providers' survey responses to schizophrenia case vignettes. In the survey, providers were randomized to vignettes with access to PDAI and with no access. In the third step, the economic implications of alternative provider decisions were identified from published peer-reviewed studies. The simulation model calculated the total economic value of PDAI as the difference between expected annual patient total cost corresponding to provider decisions made with or without PDAI.

RESULTS

In claims data, 75.3% of patients with schizophrenia were found to be nonadherent to their antipsychotic medications. Review of the literature revealed that 7% of patients cannot tolerate medication, and 72.9% would respond to antipsychotic medication if adherent. Survey responses by providers (n = 219) showed that access to PDAI would significantly alter treatment decisions for nonadherent or adherent/poorly controlled patients (P < 0.001). Payers can expect to save $3,560 annually per nonadherent patient who would respond to therapy if adherent. Savings increased to $9,107 per nonadherent patient when PDAI was given to providers who frequently augmented therapy for these patients. Among all poorly controlled patients (i.e., the nonadherent or those who were adherent but unresponsive to therapy), access to PDAI decreased annual patient cost by $2,232. Savings for this group increased to $7,124 per patient when PDAI was given to providers who frequently augmented therapy.

CONCLUSIONS

Access to PDAI significantly improved provider decision making, leading to lower annual health care costs for patients who were nonadherent or adherent but poorly controlled. Additional research is warranted to evaluate how new technologies that accurately monitor adherence would affect health and economic outcomes among patients with serious mental illness.

DISCLOSURES

This study and medical writing assistance was funded by Otsuka Pharmaceutical Development & Commercialization. Shafrin and Schwartz are employees of Precision Health Economics, which received funding from Otsuka Pharmaceutical Development & Commercialization in support of this study. Lakdawalla is Chief Scientific Officer and a founding partner of Precision Health Economics. Schwartz is a consultant for Otsuka Pharmaceutical Development & Commercialization, and Forma is an employee of Otsuka Pharmaceutical Development & Commercialization. The authors presented the abstract for this study as a poster presentation at the AMCP Managed Care & Specialty Pharmacy Annual Meeting, April 19-22, 2016, San Francisco, California. All authors contributed equally to the study design, data collection and analysis, and the writing and revision of the manuscript.

摘要

背景

精神分裂症患者不遵医嘱服用抗精神病药物会导致症状管理不善,增加医疗保健和其他费用。尽管对健康有影响,但药物依从性仍然难以准确评估。新技术提供了实时监测患者用药依从性数据的可能性,这反过来可能改善临床决策。然而,准确的患者药物依从性信息(PDAI)的经济效益尚未得到评估。

目的

通过改善精神分裂症患者治疗中医疗服务提供者的决策,量化更准确的 PDAI 如何为支付者创造价值。

方法

使用三步决策树建模框架来衡量 PDAI 对开始使用非典型抗精神病药物治疗的精神分裂症患者的年度成本(2016 年美元)的影响。第一步根据 3 个属性对患者进行分类:抗精神病药物的依从性、药物耐受性和对治疗的反应取决于药物的依从性。每个特征的流行率是从索赔数据库分析和文献综述中确定的。第二步基于医疗服务提供者对精神分裂症病例描述的调查回复,模拟 PDAI 对提供者治疗决策的影响。在调查中,提供者被随机分配到有或没有 PDAI 访问权限的病例描述。第三步,从已发表的同行评议研究中确定替代提供者决策的经济影响。模拟模型计算 PDAI 的总经济价值是根据提供者有或没有 PDAI 做出决策的相应患者年度总费用之间的差异。

结果

在索赔数据中,发现 75.3%的精神分裂症患者不依从他们的抗精神病药物。文献综述显示,7%的患者不能耐受药物,如果依从,72.9%的患者会对抗精神病药物有反应。提供者(n=219)的调查回复显示,获得 PDAI 将显著改变不依从或依从/控制不佳患者的治疗决策(P<0.001)。如果依从的话,不依从患者的年人均节省费用为 3560 美元。当 PDAI 提供给经常为这些患者增加治疗的提供者时,不依从患者的年人均节省费用增加到 9107 美元。在所有控制不佳的患者(即不依从或依从但对治疗无反应的患者)中,获得 PDAI 可使患者的年度医疗成本降低 2232 美元。当 PDAI 提供给经常增加治疗的提供者时,这一人群的患者年人均节省费用增加到 7124 美元。

结论

获得 PDAI 显著改善了提供者的决策,导致不依从或依从但控制不佳的患者的年度医疗成本降低。需要进一步研究新技术如何准确监测依从性,以评估其对严重精神疾病患者的健康和经济结果的影响。

披露

这项研究和医学写作援助由大冢制药研发与商业化公司资助。Shafrin 和 Schwartz 是 Precision Health Economics 的员工,该公司从大冢制药研发与商业化公司获得资助,以支持这项研究。Lakdawalla 是 Precision Health Economics 的首席科学官和创始合伙人。Schwartz 是大冢制药研发与商业化公司的顾问,Forma 是大冢制药研发与商业化公司的员工。作者以海报的形式在 2016 年 4 月 19 日至 22 日在旧金山举行的 AMCP 管理式医疗和专科药房年会(AMCP Managed Care & Specialty Pharmacy Annual Meeting)上展示了这项研究的摘要。所有作者都对研究设计、数据收集和分析以及论文的撰写和修订做出了同等贡献。

相似文献

引用本文的文献

5
Human factors evaluation of a novel digital medicine system in psychiatry.一种新型精神科数字医学系统的人为因素评估
Neuropsychiatr Dis Treat. 2018 Feb 16;14:553-565. doi: 10.2147/NDT.S157102. eCollection 2018.

本文引用的文献

1
Optimization of a Digital Medicine System in Psychiatry.精神科数字医学系统的优化
J Clin Psychiatry. 2016 Sep;77(9):e1101-e1107. doi: 10.4088/JCP.16m10693.
7
An ingestible sensor for measuring medication adherence.一种用于测量药物依从性的可摄入传感器。
IEEE Trans Biomed Eng. 2015 Jan;62(1):99-109. doi: 10.1109/TBME.2014.2341272.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验