Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, NY, USA.
Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY, USA.
J Am Med Inform Assoc. 2020 May 1;27(5):709-716. doi: 10.1093/jamia/ocaa021.
Predictive analytics are potentially powerful tools, but to improve healthcare delivery, they must be carefully integrated into healthcare organizations. Our objective was to identify facilitators, challenges, and recommendations for implementing a novel predictive algorithm which aims to prospectively identify patients with high preventable utilization to proactively involve them in preventative interventions.
In preparation for implementing the predictive algorithm in 3 organizations, we interviewed 3 stakeholder groups: health systems operations (eg, chief medical officers, department chairs), informatics personnel, and potential end users (eg, physicians, nurses, social workers). We applied thematic analysis to derive key themes and categorize them into the dimensions of Sittig and Singh's original sociotechnical model for studying health information technology in complex adaptive healthcare systems. Recruiting and analysis were conducted iteratively until thematic saturation was achieved.
Forty-nine interviews were conducted in 3 healthcare organizations. Technical components of the implementation (hardware and software) raised fewer concerns than alignment with sociotechnical factors. Stakeholders wanted decision support based on the algorithm to be clear and actionable and incorporated into current workflows. However, how to make this disease-independent classification tool actionable was perceived as a challenge, and appropriate patient interventions informed by the algorithm appeared likely to require substantial external and institutional resources. Stakeholders also described the criticality of trust, credibility, and interpretability of the predictive algorithm.
Although predictive analytics can classify patients with high accuracy, they cannot advance healthcare processes and outcomes without careful implementation that takes into account the sociotechnical system. Key stakeholders have strong perceptions about facilitators and challenges to shape successful implementation.
预测分析是一种很有潜力的工具，但要改善医疗服务，就必须将其谨慎地融入医疗保健组织中。我们的目的是确定在 3 家医疗机构实施一种新颖的预测算法的促进因素、挑战和建议，该算法旨在前瞻性地识别具有高可预防利用率的患者，以便主动让他们参与预防干预。
在准备将预测算法应用于 3 家机构时，我们对 3 个利益相关者群体进行了访谈：医疗系统运营人员（例如首席医疗官、部门主管）、信息人员和潜在的最终用户（例如医生、护士、社工）。我们应用主题分析得出关键主题，并将其归类为 Sittig 和 Singh 原始社会技术模型的维度，以研究复杂自适应医疗保健系统中的健康信息技术。招募和分析是迭代进行的，直到达到主题饱和。
在 3 家医疗机构进行了 49 次访谈。实施的技术组件（硬件和软件）引起的关注少于与社会技术因素的一致性。利益相关者希望基于算法的决策支持清晰且可操作，并纳入当前的工作流程。然而，如何使这种独立于疾病的分类工具具有可操作性被认为是一个挑战，并且由算法提供的适当的患者干预措施似乎需要大量的外部和机构资源。利益相关者还描述了预测算法的信任、可信度和可解释性的重要性。
尽管预测分析可以非常准确地对患者进行分类，但如果不考虑社会技术系统，精心实施这些分析将无法推进医疗服务流程和结果。关键利益相关者对成功实施的促进因素和挑战有强烈的看法。
