The Healthcare Analytics Adoption Model: A Roadmap to Analytic Maturity

This report is based on a 2020 webinar given by Dale Sanders, Chief Technology Officer, Health Catalyst, entitled, “Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for Analytic Maturity”.

Across the country, health systems are losing providers due to burnout. As leaders in healthcare know, data and analytics can help the situation or exacerbate it. Although analytics play a critical role in healthcare improvement, in some cases the focus of data can actually contribute to burnout through the increasing “EHR problem”—prioritizing the EHR over patients.

Danielle Ofri, MD, atBellevue Hospitalposes a question to healthcare providers everywhere, “The aggressiveness of the EMR’s incursion into the doctor–patient relationship has forced us to declare our loyalties: are we taking care of patients or are we taking care of the EMR?”

Data Analytics Is Part of the Problem

电子人力资源主管要承担责任，但过度关注数据也是问题的一部分。The proliferation of all quality measures and reporting—and the burden that it places on physicians—is doing the opposite of what health systems are trying to achieve withdata. Rather than simplifying processes and improving care, the focus on data can make care delivery unnecessarily complex, resulting in wasted resources as a health system tries to become data driven.

The Healthcare Analytics Adoption Model (HAAM) provides healthcare organizations with a framework to follow in order to fully leverage the capabilities of analytics and achieve the primary goals of using data in healthcare— to improve patient outcomes while cutting costs, decreasing provider burnout, and maintaining patient satisfaction.

Sanders’s Hierarchy of Analytics Needs 2002

2002年，HAAM框架(图1)是Dale Sanders的，他现在是Health Catalyst的首席技术官，是指导卫生系统实现分析成熟度的第一个模型。世界杯葡萄牙vs加纳即时走地At step one, health systems needed to focus on adhering to compliance and regulatory measures, then accreditation (e.g.,Joint Commission). Then, systems could focus on accreditation and professional societies, (e.g.,STS, etc.,) followed by meeting payer financial incentives. After that, the focus turned to implementing evidence-based medicine routinely practiced throughout the organization.

As a first attempt at creating an analytics adoption model, it captured the basics, but lacked definition and left room for growth as the industry expanded/evolved overtime.

Healthcare Analytics Adoption Model, 2013

By 2013, as health systems were maturing and leveraging analytics at a deeper level, the HAAM followed suit by adding three more levels that focused on automation, machine learning (ML), and a more individualized approach to medicine.

The model also matured to emphasize the basics for analytics acumen—levels three and four, the basic internal and external reporting requirements—before moving to levels five and above. The inefficiencies and overwhelming number of quality measures and KPIs in levels three and four were a roadblock for many health systems, so it was critical to increase the efficiency and the productivity at levels four and below in order to move forward.

HIMSS and Health Catalyst Adoption Model for Analytic Maturity, 2015

In 2015,世界杯葡萄牙vs加纳即时走地andHealthcare Information and Management Systems Society(HIMSS) saw the need to create a standard model for analytic maturity, so they teamed up to create an official Adoption Model for Analytic Maturity (Figure 3) that could serve as the industry standard—disconnected from any vendor.

HAAM 2019

The latest version of HAAM (Figure 4) is based largely on the original model. Currently, Dr. Anne Snowdon, Director of Clinical Research at HIMSS Analytics, and Dale Sanders are working together to ensure the modifications are collaborative and supportive of HIMSS’s initiatives, by adding Level 9—focused on increasing patients’ analytics acumen and helping them become data-driven when it comes to their own care.

The Healthcare Analytics Adoption Model at Every Level

Level 0. Fragmented Point Solutions

Vendor-based and internally developed applications are used to address specific analytic needs as they arise, but the fragmented point solutions are neither co-located in adata warehousenor otherwise architecturally integrated with one another. Overlapping data content leads to multiple versions of analytic truth, making reporting labor-intensive and inconsistent. At this point, data governance is non-existent.

1级。Enterprise Data Operating System

At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3 data, revenue cycle, financial, costing, supply chain, and patient experience. A searchable metadata repository is available across the enterprise. Data content includes insurance claims, if possible. The data warehouse is updated within one month of source system changes and data governance is forming around the data quality of source systems. The enterprise data warehouse reports organizationally to the chief information officer.

级别2。标准化词汇和患者登记

Master vocabulary and reference data are identified and standardized across disparate source system content in the data warehouse. Naming, definition, and data types are consistent with local standards and patient registries are defined solely on ICD billing data. Data governance forms around the definition and evolution of patient registries and master data management.

Level 3. Automated Internal Reporting

分析动机集中于一致、高效地生成报告，以支持医疗保健组织的基本管理和运营。从执行层到一线经理都很容易了解关键的绩效指标。公司和业务部门的数据分析师定期开会，以协作和指导EDW。数据治理扩展为提高组织的数据素养，并为4级及以上级别制定数据获取策略。

Level 4. Automated External Reporting

The analytic motive is focused on consistent, efficient production of reports required for regulatory and accreditation requirements (e.g., CMS, Joint Commission, tumor registry, communicable diseases); payer incentives (e.g., MU, PQRS, VBP, readmission reduction); and specialty society databases (e.g., STS, NRMI, Vermont-Oxford). Adherence to industry-standard vocabularies is required at this point. Clinical text data content is available for simple keyword searches and centralized data governance exists for review and approval of externally released data.

Level 5. Waste and Care Variability Reduction

分析动机集中于衡量临床最佳实践的依从性，最小化浪费，并减少可变性。At this point, data governance expands to support care management teams that are focused on improving the health of patient populations, population-based analytics are used to suggest improvements to individual patient care and permanent multidisciplinary teams are in-place that continuously monitor opportunities to improve quality and reduce risk and cost, across acute careprocesses、慢性疾病、患者安全场景和内部工作流程。通过在患者队列的定义中包括来自实验室、药房和临床观察的数据，可以提高注册的准确性。EDW内容被组织成基于证据的标准化数据集市，其中结合了与患者注册相关的临床和成本数据。数据内容扩展到包括保险索赔(如果还没有包括)和HIE数据feed，并且在源系统更改后的一周内更新EDW。

Level 6. Population Health Management and Suggestive Analytics

The “accountable care organization” shares in the financial risk and reward that is tied to clinical outcomes. At least 50% of acute care cases are managed under bundled payments. Analytics are available at the point of care to support the Triple Aim of maximizing the quality of individual patient care, population management, and the economics of care. Data content expands to include bedside devices, home monitoring data, external pharmacy data, and detailed activity-based costing. Data governance plays a major role in the accuracy of metrics supporting quality-based compensation plans for clinicians and executives. On average, the EDW is updated within one day of source system changes. The EDW reports organizationally to a C-level executive who is accountable for balancing cost of care and quality of care.

7级。Clinical Risk Intervention and Predictive Analytics

Analytic motive expands to address diagnosis-based, fixed-fee per capita reimbursement models. Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling, forecasting, and risk stratification to support outreach, triage, escalation, and referrals. Physicians, hospitals, employers, payers, and members/patients collaborate to share risk and reward (e.g., financial reward to patients for healthy behavior). Patients are flagged in registries who are unable or unwilling to participate in care protocols. Data content expands to include home monitoring data, long term care facility data, and protocol-specific patient-reported outcomes. On average, the EDW is updated within one hour or less of source system changes.

Level 8. Personalized Medicine & Prescriptive Analytics

Analytic motive expands to wellness management, physical and behavioral functional health, and mass customization of care. Analytics expands to include NLP of text, prescriptive analytics, and interventional decision support. Prescriptive analytics are available at the point of care to improve patient-specific outcomes based upon population outcomes. Data content expands to include 7×24 biometrics data, genomic data, and familial data. The EDW is updated within a few minutes of changes in the source systems.

Level 9. Direct-To-Patient Analytics & Artificial Intelligence

分析和人工智能(AI)直接提供给患者，这使他们的健康决策更具有个人所有权和准确性。随着更多的知识和参与到他们的护理中，患者和他们的医疗团队成为数据驱动的。

直接针对患者的分析和AI被用于患者和医疗保健提供者之间的协作决策环境。患者有能力移植和分析他们完整的医疗保健数据生态系统，独立于医疗保健提供商。治疗和健康维护协议使用基于人工智能的数字双胞胎——“病人喜欢这个”和“病人喜欢我”模式识别。

The Healthcare Analytics Adoption Model: Key to Analytic Maturity

卫生系统知道，准确的数据对于成为数据驱动型组织至关重要，但实现可持续和有效采用分析的唯一途径是遵循一个经过验证的框架。HAAM是专门为医疗保健组织及其在数据方面所面临的独特挑战而设计的(自2002年以来经过多次迭代)。

Following the HAAM, healthcare organizations can build a sustainable, analytic platform to leverage analytic–a must-do in order to survive asreimbursementsand measures shift the focus from volume tovalue. With a strong analytic framework, health systems can leverage data to drive outcomes improvement and stay focused on patients instead of the EHR.

Additional Reading

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1. Improving Strategic Engagement for Healthcare CIOs with Five Key Questions
2. How to Build a Healthcare Analytics Team and Solve Strategic Problems
3. How to Run Your Healthcare Analytics Operation Like a Business
4. Three Must-Haves for a Successful Healthcare Data Strategy