Healthcare Analytics Adoption Model

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Why Healthcare Analytics?

Healthcare in the United States and other parts of the world has slowly been progressing through three waves of data management: data collection, data sharing, and data analytics. So far, the data collection and sharing waves, characterized by the urgent deployment of EHRs and health information exchanges, have failed to significantly impact the quality and cost of healthcare. In some cases, this nonstop emphasis on data has contributed to an over focus on the EHR, contributing to provider burnout and time and attention away from patients.

Despite the current hype about big data being the next “big” thing in other industries, in healthcare we are just beginning to have the necessary analytics capabilities that enable system-wide quality improvement and cost reduction efforts.

Healthcare analytics is the systematic use of data to create meaningful insights. The real promise of analytics lies in its ability to transform healthcare into a data-driven culture, powered by a world-class analytics platforms, like the Health Catalyst Data Operating System (DOS™).

The Healthcare Analytics Adoption Model

Healthcare data and analytics can be confusing and overwhelming without a framework to guide your approach and priorities. Because the healthcare industry lacked a comprehensive analytics model that fit the unique needs of healthcare data, a group of cross-industry healthcare veterans created the Healthcare Analytics Adoption Model.

Health organizations can reference the model throughout its analytics journey, as it provides specific guidance on classifying groups of analytics capabilities and provides systematic sequencing to adopting analytics within the health organization. It is critical for health systems to follow some type of analytics model because the right model will lay the foundation for a successful, sustainable analytics strategy that will support more complex data needs in the future.

A Framework to Develop Analytics Maturity

The Healthcare Analytics Adoption Model has evolved from a 5-step framework in 2002 to an 8-step model in 2012, combining lessons learned from analytics experts at Health Catalyst and Healthcare Information and Management Systems Society (HIMSS). Finally, in 2019, the latest version of the Healthcare Analytics Adoption Model, very similar to the original model, was released with an added level 9—a focus on developing patients’ analytics understanding so that patients and their care teams are making data-driven decisions together—to adapt to the ever-changing needs integrating data into healthcare.

The Healthcare Analytics Adoption Model provides three major benefits to health systems looking to grow in analytics maturity:

A framework for evaluating the industry’s adoption of analytics. A roadmap to measure progress toward analytic adoption. A framework for evaluating vendor products.

When health systems leverage the Healthcare Analytics Adoption Model to its full potential, and follow it closely step by step, they will fully understand and leverage the capabilities of their analytics and achieve the ultimate goal that has eluded most provider organizations—improve the quality of care while lowering costs and enhancing clinician and patient satisfaction.

The Nine Levels of the Analytics Adoption Model

(click to reveal each level)

Level 9 – Direct-to-Patient Analytics & AI Direct-to-patient analytics & AI Analytics and AI are provided directly to patients which enables greater personal ownership and precision in their health decisions: 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. Level 8 – Personalized Medicine & Prescriptive Analytics Contracting for & managing health 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 7 – Clinical Risk Intervention & Predictive Analytics Taking more financial risk & managing it proactively Clinical Risk Intervention & 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 6 – Population Health Management & Suggestive Analytics Taking financial risk and preparing your culture for the next levels of analytics Population Health Management & 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. Level 5 – Waste & Care Variability Reduction Measuring & managing evidence based care Clinical Effectiveness & Accountable Care: Analytic motive is focused on measuring adherence to clinical best practices, minimizing waste, and reducing variability.

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.

Permanent multidisciplinary teams are in-place that continuously monitor opportunities to improve quality, and reduce risk and cost, across acute care processes, 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.

On average, the EDW is updated within one week of source system changes. Level 4 – Automated External Reporting Efficient, consistent production and agility Automated External Reporting: 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.

Clinical text data content is available for simple key word searches.

Centralized data governance exists for review and approval of externally released data. Level 3 – Automated Internal Reporting Efficient, consistent production Automated Internal Reporting: Analytic motive is focused on consistent, efficient production of reports supporting basic management and operation of the healthcare organization.

Key performance indicators are easily accessible from the executive level to the front-line manager.

Corporate and business unit data analysts meet regularly to collaborate and steer the EDW.

Data governance expands to raise the data literacy of the organization and develop a data acquisition strategy for Levels 4 and above. Level 2 – Standardized Vocabulary & Patient Registries Relating and organizing the core data Standardized Vocabulary & Patient Registries: Master vocabulary and reference data identified and standardized across disparate source system content in the data warehouse.

Naming, definition, and data types are consistent with local standards.

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 1 – Enterprise Data Warehouse Foundation of data and technology Enterprise Data Warehouse: 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.

Searchable metadata repository is available across the enterprise.

Data content includes insurance claims, if possible.

Data warehouse is updated within one month of source system changes.

Data governance is forming around the data quality of source systems.

The EDW reports organizationally to the CIO. Level 0 – Fragmented Point Solutions Inefficient, inconsistent versions of the truth Fragmented Point Solutions: Vendor-based and internally developed applications are used to address specific analytic needs as they arise.

The fragmented point solutions are neither co-located in a data warehouse nor otherwise architecturally integrated with one another.

Overlapping data content leads to multiple versions of analytic truth.

Reports are labor intensive and inconsistent.

Data governance is non-existent.

Learn More About Each Level

View Dale’s Healthcare Analytics Adoption Model webinar and download his presentation slides and transcript.

Read an in-depth Healthcare Analytics Adoption Model article with a detailed explanation of each of the nine levels.

Find Out Where You Stand: Take the Self-Assessment Survey

Similar to the HIMSS EHR Adoption Model, there is a needed logical progression to become a systemic, analytics-driven organization. Health systems that aspire to higher-level results which attempt to tackle analytics in a scattered way are often frustrated by the lack of inadequate foundational platforms, tools, and skills that need to be mastered at the lower levels of the analytics adoption model. Based on our years of collective experience, we have designed a self-assessment survey to help you assess where your organization is consistently operating in each of the levels. Upon finishing the survey, you will receive a customized report either in HTML format or a PDF version that you can email to yourself, summarizing your status and a list of customized recommendations, based on your input.

Healthcare Analytics Is Evolving

The Health Catalyst Data Operating System (DOS™) is a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform. DOS™ is our response to a future of healthcare centered around the broad and more effective use of data.

Read More About Healthcare Analytics

The Analytics Adoption Model White Paper

Dale Sanders, Chief Technology Officer

Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Sciences Market

Dale Sanders, Chief Technology Officer

The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution

Dale Sanders, Chief Technology Officer

The Healthcare Analytics Ecosystem: A Must-Have in Today’s Transformation

John Wadsworth, Vice President, Technical Operations

The Six Biggest Problems with Homegrown Healthcare Analytics Platforms

Ryan Smith, Senior Vice President and Executive Advisor

Three Must-Haves for a Successful Healthcare Data Strategy

David Grauer, Senior Vice President, Professional Services

Two Helpful Webinars

The Analytics Adoption Model Explained (On Demand Webinar, Slides, and Transcript)

Dale Sanders, Chief Technology Officer

A Reference Architecture For Digital Health: The Health Catalyst Data Operating System (On Demand Webinar, Slides, and Transcript)

Dale Sanders, Chief Technology Officer

PowerPoint Slides

Would you like to use or share these concepts? Download this Healthcare Analytics Adoption Model presentation highlighting the key main points.

Click Here to Download the Slides