The Core Engine of Healthcare 2.0 is Analytics
Healthcare 1.0 was broadly defined by a focus on defensive medicine, billing, and fee-for-service, culminating in the mass adoption of EMRs and data proliferation. So far, these efforts have failed to significantly impact the quality and cost of healthcare.
Healthcare 2.0 is a new wave focused on improving clinical efficiency, quality of care, affordability, and fee-for-value – culminating in a new age of healthcare analytics. Analytics will be central to achieving the systematic quality improvements and cost reductions demanded by healthcare reform. This new age of analytics will require a foundational set of analytic information systems that many executives have not anticipated.
The Healthcare Analytics Adoption Model
Many systems and vendors are talking about healthcare analytics as the “next big thing.” Unfortunately, many systems are attempting to implement analytics in a scattered, haphazard way. And many vendors are selling short-term point solutions which address a narrow set of needs, but do not establish the foundational approach to analytics that will sustainable and needed to survive and thrive in the upcoming healthcare reform market.
Health Catalyst has adopted the Healthcare Analytic Adoption Model (Source: Electronic Healthcare 2012), developed by a cross-industry group of healthcare industry veterans, as a framework to guide our platform and applications development. View Contributors List. This framework has been tested and shown to work in the most successful, analytic-oriented healthcare systems in the country. When followed systematically, it can help any health system develop a sustainable approach to analytics.
The model borrows lessons learned from the HIMSS EMR Adoption Model, and describes an analogous approach for assessing the adoption of analytics in healthcare. The Healthcare Analytic Adoption Model provides:
- A framework for evaluating the industry’s adoption of analytics
- A roadmap for organizations to measure their own progress toward analytic adoption
- A framework for evaluating vendor products
We believe that this Analytics Adoption Model will enable healthcare organizations to fully understand and leverage the capabilities of analytics and so achieve the ultimate goal that has eluded most provider organizations – that of improving the quality of care while lowering costs and enhancing clinician and patient satisfaction.
The Eight Levels of the Analytics Adoption Model
The Analytics Adoption Model contains the following 8 levels.
(Click on any of the levels to reveal the specific descriptions of the model.)
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 7x24 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 and 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.
A Range of Products Across Analytics Stages
Health Catalyst has thoughtfully developed a range of products, applications, and services that span the different stages and levels of the Analytic Adoption Model. No matter where a system has progressed in analytics, Catalyst has a set of products that is optimized for your current environment and scalable to meet your future analytics needs, at the pace your health system selects. We divide our analytic products and services into three main categories
Our best advice on healthcare analytics comes from our experts at Health Catalyst
At Health Catalyst, we have many experts with years of practical experience and expertise. We get asked all the time to share our learnings and best practices with many different healthcare professionals. The following are a few of our most requested topics and subjects. We hope these may be helpful to you as you implement healthcare analytics in your environment.
- Questions You Should Ask When Selecting a Healthcare Analytics Platform
Author: Eric Just
- The Best Healthcare Analytics Approach
Author: Tom Burton
- A Review of Approaches to Healthcare Analytics: What Doesn’t Work and What Does
Author: Russ Staheli
- How Health Catalyst Uses the Analytics Adoption Model to Develop Analytical Applications
Author: Jared Crapo
- Leveraging Healthcare Analytics to Reduce Heart Failure Readmission Rates
Author: Kathy Merkley
- The Best Healthcare Analytics Application for Prioritizing Improvement Programs
Author: Bobbi Brown
- The Best Organizational Structure for Healthcare Analytics: Sustaining Quality and Cost Improvements
Author: John Wadsworth
- 3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve Healthcare’s Problems
Author: Dale Sanders
- Problem Solved: How Analytics Will Lower Waste and Reduce Costs for the Healthcare Industry
Author: Bobbi Brown
Customer Successes using Healthcare Analytics
- Improving Population Health Through the Use of Data and Reporting
Enterprise data warehouse, analytics and patient satisfaction scores enable performance reporting and shared decision making.
- How to Reduce 30-day Heart Failure Readmission Rates
A 63 percent increase in post-discharge medication reconciliation
- Hospital Acquired Infections – How to Reduce Surveillance Waste
Infection preventionists can now focus on interventions vs. data gathering. Electronic algorithm for reporting to the National Healthcare Safety Network (NHSN)
- Texas Children’s Hospital: How to improve quality with an EDW
Advanced Healthcare Analytics and a Late-Binding (TM) Data Warehouse Prove Critical to Improving Quality at Texas Children’s Hospital