# Presentation: 015756

## Potential Plan Responses to GenomicsClinical Plan Decision Support Activities Evolutionary Paths Forward

- Potential Plan Responses to GenomicsClinical Plan Decision Support Activities Evolutionary Paths Forward

## Plan Perspectives on Genomics and Personalized Health

*Genomics may make clinical processes  more precise, specific to the Individual, and thus higher in quality.  This will influence traditional plan based cost and quality management tools *

**Genomic Technology:**
*Genomics will likely transform the practice of medicine to a more individualized approach as this new knowledge base matures into clinical tools: Personalized Evidence Base*

*Genomic tests and interventions are still in an early stage and their exact role in the clinical armamentarium is evolving *

**Plan Implication:**
*Transformation of medical practice may create fundamental changes to health plan operations and insurance functions*

*Traditional tools**** ****such as contracting and formularies may become less effective as these strategies interact with genetic variation and individualized therapeutic approaches *

*Plan processes such as Medical Policy and Technology assessments are focused on ensuring genomic tests produce actionable information*

- The size and scope of genomic medicine implications for health care
- quality and costs requires a consideration of health plan perspectives

## Health Plan Primary Challenge: Affordability

- Innovations are a leading driver of premium inflation today.
- Genomic Medicine may exacerbate this trend.

**Notes:**
Chart is on the increase in premiums, not how the health care dollar is spent

Re. how the healthcare dollar is spent:
  Anthem-- approximately 82% is spent on claims, and the remainder 18% is administrative costs (4% on profit)
 Administrative costs are lower than United (18.3%), Aetna (24.6%), and Cigna (29.8%), while Humana is slightly lower than Anthem at16.3%.

Don’t expect a  slow down in health care delivery costs to have a big impact on premiums, since other factors (specifically legislation, inflation, and consumer demand) are more difficult to control.

## Physician Challenge: Providing Quality Care

**Clinical decision support tools from health plans have focused on improving evidence**
**base compliance and optimum management of health plan benefits by leveraging **
**claim data (and increasingly clinical data) and messaging physicians and members**

- According to leading health service and health policy researchers, there is a significant gap in the care that doctors provide and the care that the evidence base says they ought to provide*
- 40% of people with chronic disease don’t get recommended care
- 50% of people don’t receive recommended preventive care
- 30% receive contraindicated acute care
- 20% receive contraindicated chronic care
- “Increasing compliance with what we already know to be the right thing to do is a far bigger challenge than figuring what the right thing to do is”**

- Genomics, by creating a much larger Personalized Evidence Base of ‘the right things to do’, could exacerbate this problem.

- *Schuster, McGlynn and Brook.
- Milbank Quarterly.; 76(4) 1998: 517-563

- *Earl Steinberg MD
- CEO Resolution Health and noted John Hopkins Health Services Researcher.

## Current Model:Generalized Knowledge Applied to Episodic Care

**Institutional Health Care**

- Current Model:Generalized Knowledge Applied to Episodic Care

-  **Application** of **Generalized** **Clinical Evidence Base** by **Clinician** ***Episodic Care***

**Generalized** **Research**
- ↓
**Generalized** **Knowledge**

- Dissemination & Training of Clinicians in Generalized **CONTENT**

**Generalized** **Clinical Evidence Base**
*“**What ought to be done in general”*

- **Individual** Health & Needs at the Time of Care

- Fragmentation of individual care = ineffective & inefficient care
- Failure to apply general evidence base

**Health IT** should help:
- integrate individual care across institutions
- support use of generalized knowledge (CDS)
- automate current processes
**(BUT we can and do regrettably  ‘muddle through without doing any of these.**’)

## Moving ForwardChanging the Plan’s Role in Health Care Decision Making

- Moving ForwardChanging the Plan’s Role in Health Care Decision Making

_*TODAY*_*, Plan care management functions influence physician and member behavior *_*AFTER*_* a care decision has been made.*
*Results**: Member and physician dissatisfaction, admin costs, and sub optimal impacts to cost and quality*

_*MUST COME SOON*_*, Plan medical management activities to influence decisions *_*AT THE TIME*_* they are taken.*
- Results: Plan benefits via reduced admin costs, member satisfaction is improved and physician abrasion goes down. Quality and safety of care improves via clinical decision support algorithms at point of care.

*Utilization Mgmt*

*Clinical Decision Support for Members and Clinicians*

*Disease Mgmt*

*Formulary Mgmt*

*Radiology Mgmt*

*Plan Med Mgmt*

*Physician-Patient Interaction*

**Health I.T.**

## Moving Forward With Health I.T.Plan Supported Components

- Moving Forward With Health I.T.Plan Supported Components

- Clinician Components

- Basic EHR

**Data Warehouse with clinical data enhancements**: (lab, radiology, PHR, HRA, carve outs)

- Clinical Decision Support
- Clinical & Financial
- Algorithms

- Outcomes Research

- Member Components

- New Knowledge
- Review of existing knowledge

**Individual Health Record encompassing PHR, E-HR data sets linked to**
**associated functionality provides a foundation for clinical decision support**

- Tools to Support Health & Care Delivery

## Clinical Decision Support: Proof of Effectiveness Today

- Clinical Decision Support: Proof of Effectiveness Today

**E-Prescribing clinical decision support alerts (safety) are causing physicians to change the prescription for 2-10% of all alerts fired **
** **
**E-prescribing formulary alerts and therapeutic alternative prompts are creating a 2% gain in generic utilization and formulary adherence rates **
**Clinical studies reviewing personal care notes mailed to members identifying evidence based care gaps caused an 11% gain in adherence to evidence based medicine practices in interventional group vs. controls**

- Health plans are building an evidence base that indicates clinical decision support;
- even in relatively rudimentary forms can impact practice patterns

## Decision Support: User Perspectives Today

- Clinical Decision Support is aimed at improving effectiveness and efficiency of current practice
- Lack of tool maturity, physician adoption, and inconsistent incentives are key challenges
- Clinical Decision Support is not yet a proven value generator for most health plans:
- Lack of accepted ROI but evidence pointing in right direction
- CDS funding amounts reflective of pilot status for most plans
- Greater funding emphasis on building infrastructure for data sharing, improving breadth and depth of solutions
- Health plans generally do not participate in basic research and only the largest health plans support applied research projects directly such as outcomes research projects
- Many health plans have launched demonstration projects however the number of these projects which include clinical decision support (excluding E-prescribing) is quite small

## And Then Comes ... Personalized Health Care

**Genomics will allow for a **_**Personalized Evidence Base**_** for each of us**
***What works for ‘me’ and not just what works ‘in general’***
**Clinicians will need to have access to and act upon the *****Individual’s***** Personalized Health Evidence Base (the ‘record’)**
**Members will need to know and act upon their own *****Personalized*****  *****Individual *****Health Evidence Base**
**This greatly increase the variants on what ‘ought to happen’**
**Information tools and decision support beyond today’s EHRs and PHRs will be necessary for the implementation of Personalized Health Care**

## Personalized Medicine Evidence Base Applied to Individual Lifestyle, Health and Care

**Individual Health****and Lifestyle**

**Institutional Health Care**

-  **Application** of **Personalized** **Evidence Base **by** Clinician*****“Episodic Care”***

**Health IT** **MUST**:
- Maintain Personalized Evidence Base for each Individual
- Manage application of Personalized Evidence by each Individual to his or her own health and lifestyle
- Support application of Personalized Evidence at the time of Professional health care anywhere
**We CANNOT ‘muddle through’ anymore in this scenario**

- **Personalized Genomic Evidence Base **&** Advice**
***“******What you ought to do”***

- **Application** of **Personalized** **Evidence** **Base** by _**Individual**_
***“******What I am doing”***
***“******Continuous Health”***

## User Perspectives on Decision Support for Personalized Health Care

- New (Health) Decision Support Tools are necessary for the implementation of personalized Health Care
- Key Steps Required:
- Expanded outcomes research studies to refine clinical value of Personalized Health Care
- A generally accepted Personalized Evidence Base that can be adopted into algorithms developed by broadly recognized entity
- Health Information Technology infrastructure deployed with solutions that meaningfully support personalized care processes for both clinicians and individuals, using a common platform, ontology, and concurrent rules monitoring
- Incentives for clinicians and individuals to adopt the health care and technical infrastructure needed to implement Decision Support

## Use of Health ITCurrent and What Must Come with Personalized Medicine

- Use of Health ITCurrent and What Must Come with Personalized Medicine

**Current: Generalized Knowledge Applied to Episodic Institutional Care**
- Integrate individual care across institutions – *Interoperable Health Care*
- Support use of generalized knowledge by Professionals (e.g. CPOE) and Members
- Automate current processes
_*Desirable*_* to improve Effectiveness and Efficiency of Current Health Care*
**Desired: Personalized Evidence Base Applied to Individual Lifestyle, Health and Care**
- Maintain Personalized Evidence Base for each Individual
- Manage application by **Individual** to own health and lifestyle
- Support application by **Clinician** at time of health care
- Assess results and revise Personalized Evidence Base
_*Necessary*_* to the Viability of Personalized Health*

## Transition from Current State to Personalized Health and Care

- Integrated Institutional Care

- Fragmented Institutional Care

- Individual-based Health & Care

- Professional and Individual Information Divide

- Professional and Individual Exchanging Information

- Professional and Individual Common Information Base

- Patchy Application of Generalized Evidence Base

- Best-Practice Application of Generalized Evidence Base

- Full Application of Personalized Evidence Base

**Personalized Health and Care**

**Organization**

**Decision Making**

**Information**

**Current**

**Personalized**