Validating a data science methodology for patterns of mental health services use: The patient record of clinical experience sequence study (PROCESS)

验证心理健康服务使用模式的数据科学方法：临床经验序列研究的患者记录 (PROCESS)

• 批准号: 10237119
• 负责人: Justin K. Benzer
• 金额: --
• 依托单位: OLIN TEAGUE VETERANS CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10237119
• 关键词: Affect; Antidepressive Agents; Area; Attention; Base Sequence; Brief Psychotherapy; Caring; Clinical; Collaborations; Communication; Consensus; Consult; Continuity of Patient Care; DNA Sequence; Data; Data Science; Diagnosis; Dimensions; Distal; Ensure; Event; Feedback; Follow-Up Studies; Frequencies; Funding; Goals; Health Services; Health care facility; Health system; Healthcare Systems; Improve Access; Incentives; Infrastructure; Intake; Intervention; Interview; Leadership; Learning; Location; Measures; Medical center; Mental Depression; Mental Health; Mental Health Services; Methodology; Methods; Newly Diagnosed; Outcome; Output; Patient-Focused Outcomes; Patients; Pattern; Patterns of Care; Performance; Periodicals; Pharmaceutical Preparations; Policies; Primary Care; Procedures; Process; Productivity; Provider; Psychotherapy; Quality of Care; Records; Reporting; Research Personnel; Resource Allocation; Resources; Sampling; Savings; Sequence Analysis; Services; Social Work; Surveys; Symptoms; System; Testing; Time; Veterans; Visit; Work; administrative database; care coordination; care fragmentation; cohort; comorbidity; cost; data warehouse; discrete time; experience; health care delivery; health care service; health service use; improved; innovation; integrated care; mental representation; multidisciplinary; patient engagement; primary care setting; satisfaction; wasting

Background: An effective learning healthcare system needs measures that help managers identify how to promote system-level improvements. One opportunity to influence system level improvements is to directly measure the care sequences provided to patients that may reflect decreased efficiency and increased care fragmentation. We propose to determine whether a VA administrative data can be used to construct reliable measures of care sequences. Significance/Impact: Our innovation is in adapting a data science sequence analysis methodology to VA administrative records. This methodology has the potential to highlight care fragmentation and integration. Fragmentation is arguably the most important underemphasized goal in VA. Performance goals exist for quality of care and access, and there is a strong infrastructure for managing cost. However, there is limited focus on reducing fragmentation and improving integration, in part due to the lack of adequate measures. VA priorities include more efficient resource use. Two VHA strategies to increase efficiency are to diligently find areas of waste and correct to generate savings, and to improve the delivery of health care services by ensuring care coordination across all care settings. Sequence-based fragmentation and integration measures have the potential to directly inform these strategies. Specific Aim: The specific aim of this two-year proposal is to determine whether VA administrative data can be used to reliably measure mental health sequences of care. As an exploratory aim, we will determine whether sequences may represent care fragmentation. Methodology: We will use the VA Corporate Data Warehouse to collect evidence for internal consistency and test-retest reliability. Approximately 46,000 Veterans will be sampled in each of 6 annual cohorts (FY2013-FY2018) across 54 medical centers. We will use sequence analysis to identify clusters of similar sequences that are characterized by a common consensus sequential pattern. Internal consistency will be determined by comparing random patient samples to determine if patient sequences are more similar within consensus sequential patterns than between patterns. Test-retest reliability will compare patterns over time. We expect a step function where sequences will be similar over time, with periodic changes as capabilities improve. Generally, proximal sequences will be more similar than distal sequences. For the exploratory aim, we will calculate patient-level correlations with administrative database measures of care fragmentation and facility-level correlations with VA performance measures. A Delphi process with an expert panel will the degree to which each sequence generated by the methodology may measure fragmentation. This will provide preliminary data for the next study. Next Steps/Implementation: This two-year study will determine whether the sequence analysis method can be applied to VA administrative data to identify reliable care sequences. The output of the Delphi process and the convergent and discriminant validity tests will allow the team to develop specific hypotheses about VA depression care sequences that will be tested in a follow- up study. The next step will be to determine the association of depression care sequences with mental health symptoms, functioning, satisfaction, and cost. Our long-term goal is to develop a method for measuring care sequences in near-real time and provide feedback to managers and clinicians to identify patients regarding care sequences that may require intervention.

背景：一个有效的学习型医疗保健系统需要帮助管理者的措施 确定如何促进系统级改进。一次影响系统级别的机会 改进的目的是直接测量提供给患者的护理序列， 效率下降，护理分散化增加。我们建议确定VA是否 管理数据可用于构建护理序列的可靠测量。 意义/影响：我们的创新在于采用数据科学序列分析 方法来管理档案。这种方法有可能突出护理 分裂与整合。碎片化可以说是最重要的， 目标在VA。存在针对护理质量和可及性的绩效目标， 成本管理的基础设施。然而，对减少分散化的关注有限， 改善融合，部分原因是缺乏适当的措施。优先事项包括更多 有效利用资源。VHA提高效率的两个策略是努力寻找 浪费和纠正，以产生储蓄，并改善卫生保健服务的提供， 确保所有护理环境的护理协调。基于序列的片段化和 融合措施有可能直接为这些战略提供信息。 具体目标：这一为期两年的提案的具体目标是确定VA是否 管理数据可用于可靠地测量心理健康护理序列。作为 为了探索性的目的，我们将确定序列是否可以代表护理碎片化。 方法：我们将使用VA公司数据仓库收集内部证据， 一致性和重测信度。每年将对大约46，000名退伍军人进行抽样调查， 54家医疗中心的6个年度队列（2013财年-2018财年）。我们将使用序列分析 为了鉴定以共有序列为特征的相似序列簇， 序列模式将通过比较随机患者来确定内部一致性 样本，以确定患者序列是否在共有序列中更相似 而不是模式之间。重测信度将随着时间的推移比较模式。我们 期望一个阶跃函数，其中序列将随着时间的推移而相似，具有周期性变化， 能力提高。通常，近端序列将比远端序列更相似。 为了探索性的目的，我们将计算与管理数据库的患者水平的相关性 护理碎片化和设施水平的相关性与VA性能指标的措施。 一个带有专家小组的德尔菲过程将在一定程度上确定 方法可以衡量分散程度。这将为下一步研究提供初步数据。 下一步/实施：这项为期两年的研究将确定序列分析是否 方法可以应用于VA管理数据，以识别可靠的护理序列。输出 德尔菲法和收敛和判别有效性测试将允许团队 制定关于VA抑郁症护理序列的具体假设，这些假设将在下面进行测试- 向上学习。下一步将是确定抑郁症护理序列与 心理健康症状，功能，满意度和成本。我们的长期目标是发展一个 用于近实时地测量护理序列并向管理者提供反馈的方法， 临床医生可以识别可能需要干预的护理序列的患者。