背景:临床相关的数字健康工具的开发需要对利益相关者未满足的需求有深刻的了解,如临床医生和患者。揭示不可预见的利益相关者需求的一种方法是通过定性研究,包括利益相关者访谈。然而,传统的定性数据分析方法是耗时且资源密集的,使它们在许多构思和开发数字工具的行业环境中站不住脚。因此,我们需要一个时间效率更高的流程来确定数字工具开发的临床相关目标需求.
目的:本研究的目的是解决对可访问,简单,以及通过对半结构化访谈笔录的文本分析,对定性研究数据进行常规主题分析的高效替代方法。此外,我们试图在专家精神病学顾问访谈笔录中确定重要主题,以有效揭示针对未满足临床需求的数字工具开发领域。
方法:我们对美国治疗重度抑郁症的精神科医生进行了10次(1小时)半结构化访谈。访谈是使用访谈指南进行的,该指南包括预先设计的开放式问题,目的是(1)了解临床医生对护理管理过程的经验,以及(2)了解临床医生对患者的看法。护理管理过程。然后,我们实施了一种混合分析方法,将计算机辅助文本分析与演绎分析相结合,作为传统定性主题分析的替代方法,以识别单词组合频率。内容类别,以及描述护理管理过程中未满足需求的广泛主题。
结果:使用这种混合计算机辅助分析方法,我们确定了临床医生在重度抑郁症背景下感兴趣的几个关键领域,这些领域是数字工具开发的合适目标.
结论:将计算机辅助技术与演绎技术相结合的定性研究的混合方法提供了一种及时有效的方法来识别未满足的需求,目标,和相关主题为数字工具开发提供信息。这可以增加构建和实施有用和实用工具以最终改善患者健康结果的可能性。
BACKGROUND: The development of digital health tools that are clinically relevant requires a deep understanding of the unmet needs of stakeholders, such as clinicians and patients. One way to reveal unforeseen stakeholder needs is through qualitative research, including stakeholder interviews. However, conventional qualitative data analytical approaches are time-consuming and resource-intensive, rendering them untenable in many industry settings where digital tools are conceived of and developed. Thus, a more time-efficient process for identifying clinically relevant target needs for digital tool development is needed.
OBJECTIVE: The objective of this study was to address the need for an accessible, simple, and time-efficient alternative to conventional thematic analysis of qualitative research data through text analysis of semistructured interview transcripts. In addition, we sought to identify important themes across expert psychiatrist advisor interview transcripts to efficiently reveal areas for the development of digital tools that target unmet clinical needs.
METHODS: We conducted 10 (1-hour-long) semistructured interviews with US-based psychiatrists treating major depressive disorder. The interviews were conducted using an interview guide that comprised open-ended questions predesigned to (1) understand the clinicians\' experience of the care management process and (2) understand the clinicians\' perceptions of the patients\' experience of the care management process. We then implemented a hybrid analytical approach that combines computer-assisted text analyses with deductive analyses as an alternative to conventional qualitative thematic analysis to identify word combination frequencies, content categories, and broad themes characterizing unmet needs in the care management process.
RESULTS: Using this hybrid computer-assisted analytical approach, we were able to identify several key areas that are of interest to clinicians in the context of major depressive disorder and would be appropriate targets for digital tool development.
CONCLUSIONS: A hybrid approach to qualitative research combining computer-assisted techniques with deductive techniques provides a time-efficient approach to identifying unmet needs, targets, and relevant themes to inform digital tool development. This can increase the likelihood that useful and practical tools are built and implemented to ultimately improve health outcomes for patients.