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

The EHRs are taking the blame, but an over-focus on data is also part of the problem. 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.

TheHealthcare 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— toimprove patient outcomes同时降低成本，减少供给者的倦怠，保持病人的满意度。

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.

Figure 1. Hierarchy of Analytics Needs, 2002.

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.

Figure 2. The Healthcare Analytics Adoption Model, 2013.

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 anyvendor.

Figure 3. Adoption Model for Analytics Maturity. Source:HIMSS Continuity of Care Matrix.

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.

Figure 4. The Healthcare Analytics Adoption Model, 2019

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.

Level 1. 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 anddata governanceis 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

Analytic motive is focused on measuring adherence to clinical best practices, minimizing waste, and reducing variability. At this point, data governance expands to supportcare managementteams 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, chronic diseases, patient safety scenarios, and internal workflows. Precision of registries is improved by including data from lab, pharmacy, and clinical observations in the definition of the patient cohorts. EDW content is organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries. Data content expands to include insurance claims (if not already included) and HIE data feeds and the EDW is updated within one week of source system changes.

Level 6. Population Health Management and Suggestive Analytics

“负责任的护理组织”分担与临床结果相关的财务风险和回报。至少50%的急性护理病例通过捆绑支付方式进行管理。在护理点可用分析来支持三重目标，即最大化个体患者护理质量、人口管理和护理经济。数据内容扩展到包括床边设备、家庭监测数据、外部药房数据和详细的基于活动的成本计算。数据治理在支持临床医生和管理人员基于质量的薪酬计划的指标准确性方面发挥着重要作用。平均而言，EDW在源系统更改后的一天内更新。EDW在组织上向负责平衡护理成本和护理质量的c级主管报告。

7级。Clinical Risk Intervention and Predictive Analytics

分析动机扩展到解决基于诊断的，固定费用人均报销模型。Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling, forecasting, andrisk stratificationto 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

分析动机扩展到健康管理、身体和行为功能健康、大规模定制护理。分析扩展到包括文本的NLP、说明性分析和干涉性决策支持。规范性分析在护理点可用，以改善基于人群结果的患者特异性结果。数据内容扩展为7×24生物特征数据、基因组数据和家族数据。EDW会在源系统中的几分钟内更新。

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

Analytics andartificial intelligence(AI) are provided directly to patients, which enables greater personal ownership and precision in their health decisions. With more knowledge and involvement in their care, patients become data-driven, along with their healthcare team.

Direct-to-patient analytics and AI are used in a collaborative decision-making environment between patients and healthcare providers. Patients have the ability to port and analyze their complete healthcare data ecosystem, independent of healthcare providers. Treatment and health maintenance protocols are enabled using AI-based digital twins– “Patients Like This” and “Patients Like Me” pattern recognition.

The Healthcare Analytics Adoption Model: Key to Analytic Maturity

Health systems know that accurate data is essential to become a data-driven organization, but the only way to achieve sustainable and effective analytics adoption is to follow a proven framework. The HAAM is specifically designed—over multiple iterations since 2002—for healthcare organizations and the unique challenges they face when it comes to data.

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

你想了解更多关于这个话题吗?Here are some articles we suggest:

1. Healthcare Analytics Adoption Model: A Framework and Roadmap (white paper)
2. Improving Strategic Engagement for Healthcare CIOs with Five Key Questions
3. How to Build a Healthcare Analytics Team and Solve Strategic Problems
4. How to Run Your Healthcare Analytics Operation Like a Business
5. Three Must-Haves for a Successful Healthcare Data Strategy