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GE Healthcare Camden Group Insights Blog

Webinar Reminder: Building an Analytics-Based Value Model to Validate Transformation Investments

Posted by Matthew Smith on Nov 29, 2016 2:10:38 PM

shutterstock_392113519.jpgPlease join GE Healthcare Camden Group for a complimentary, 60-minute webinarBuilding an Analytics-Based Value Model to Validate Transformation Investments, on Thursday, December 8, 2016, at 12:00 P.M., ET.

Date:

Thursday, December 8th, 12:00 P.M., Eastern

Background:

Healthcare organizations are struggling to understand the impact of their investments in population health initiatives. To help measure performance risk and evaluate return on investment ("ROI"), organizations are building and implementing analytics-based value models as decision-making tools. Creating these value models allows healthcare organizations to quantify risks and evaluate viability. It also allows organizations to measure and track ROI in digital health technology and resources associated with various programs aimed at managing the health of the populations they serve.

GE Healthcare Camden Group's team of analysts, data scientists, and actuaries builds comprehensive analytics-based value models for organization leaders (CFO, CMO, CIO, Population Health Leaders) wanting to evaluate their ROI from investments in care management programs, look to better manage utilization, and predict outcomes.

Overview:

In this complimentary webinar, members of the GE Healthcare Camden Group team will deliver an overview of the analytics-based value model and how high-performing healthcare organizations are leveraging these to guide strategic decision making and prioritize investments in value-based care initiatives.  

Topics to be Addressed:

Held in a round-table format with GE Healthcare Camden Group senior leaders representing the roles of an organization's CFO, CMO, and CIO, the webinar will address the following questions facing today's leaders:
  • Where should organizations invest resources in order to drive the most value from their care management programs?
  • What is the impact on their programs to both acute and ambulatory utilization?
  • Which programs are driving the greatest value and how are these measured?
  • What “value levers” are important in order to drive the best outcomes?
  • What is the expected outcomes from managing certain medical conditions and/or population risk cohorts?
  • What is the typical ROI of their care management programs and population health initiatives?
  • How can this information be used to support risk-based contracting with payers and other providers?

GE Healthcare Camden Group Presenters:

Marino_Dan.jpgDaniel Marino, MBA, MHA, Executive Vice President Mr. Marino is an executive vice president with GE Healthcare Camden Group with more than 25 years of experience in the healthcare field. Mr. Marino specializes in shaping strategic initiatives for healthcare organizations and senior healthcare leaders in key areas such as population health management, clinical integration, physician alignment, and health information technology. With a comprehensive background in all aspects of practice management and hospital/physician alignment, Mr. Marino is a nationally acknowledged innovator in the development of Accountable Care Organizations and clinical integration programs.

chopra2-110511-edited-239718-edited.jpgShaillee J. Chopra, PMP, Senior Manager Ms. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models.

 

DiLoreto.pngDavid DiLoreto, M.D., MBA Dr. DiLoreto, senior vice president at GE Healthcare Camden Group, is a physician-executive who is highly experienced in executive management, strategy and operations of healthcare delivery systems, and managed care companies. He has deep management expertise in community-based and academic health systems, large group medical practices, hospitals, and managed care organizations. His areas of specialty include clinical transformation, population health, business process improvement, leadership development, medical informatics, and data management and analytics.

GreenB1.pngRobert Green, MBA, FACHE, CHFP Mr. Green is a senior vice president and the practice lead for the Financial Operations and Transactions practice. He has more than 26 years of healthcare experience with 13 years of healthcare consulting experience and 13 years of provider-based financial, operational, and strategic experience among health systems, hospitals, medical groups, management services organizations, and physician hospital organizations.

 

To Register:

To register, simply click the button below, complete a short registration form, and press the "Cick to Register!" button. You will receive a confirming email. A second email will be sent the week of December 5th with webinar login/call-in instructions.

Please note: This webinar is intended for providers, provider organizations, and industry partners. Because of the proprietarty nature of the information shared during this webinar, independent consultants and consulting agencies will not be provided access to programming. GE Healthcare Camden Group reserves to the right to limit attendance at this event. 

Value Model, Webinar, Digital Health Analytics

Questions?

Please contact Matthew Smith at msmith@ge.com

Topics: Webinar, Daniel J. Marino, Shaillee Chopra, Digital Health Services and Data Analytics, Value Model, David DiLoreto, Robert Green

New Webinar: Building an Analytics-Based Value Model to Validate Transformation Investments

Posted by Matthew Smith on Nov 16, 2016 1:16:09 PM

shutterstock_392113519.jpgPlease join GE Healthcare Camden Group for a complimentary, 60-minute webinarBuilding an Analytics-Based Value Model to Validate Transformation Investments, on Thursday, December 8, 2016, at 12:00 P.M., ET.

Date:

Thursday, December 8th, 12:00 P.M., Eastern

Background:

Healthcare organizations are struggling to understand the impact of their investments in population health initiatives. To help measure performance risk and evaluate return on investment ("ROI"), organizations are building and implementing analytics-based value models as decision-making tools. Creating these value models allows healthcare organizations to quantify risks and evaluate viability. It also allows organizations to measure and track ROI in digital health technology and resources associated with various programs aimed at managing the health of the populations they serve.

GE Healthcare Camden Group's team of analysts, data scientists, and actuaries builds comprehensive analytics-based value models for organization leaders (CFO, CMO, CIO, Population Health Leaders) wanting to evaluate their ROI from investments in care management programs, look to better manage utilization, and predict outcomes.

Overview:

In this complimentary webinar, members of the GE Healthcare Camden Group team will deliver an overview of the analytics-based value model and how high-performing healthcare organizations are leveraging these to guide strategic decision making and prioritize investments in value-based care initiatives.  

Topics to be Addressed:

Held in a round-table format with GE Healthcare Camden Group senior leaders representing the roles of an organization's CFO, CMO, and CIO, the webinar will address the following questions facing today's leaders:
  • Where should organizations invest resources in order to drive the most value from their care management programs?
  • What is the impact on their programs to both acute and ambulatory utilization?
  • Which programs are driving the greatest value and how are these measured?
  • What “value levers” are important in order to drive the best outcomes?
  • What is the expected outcomes from managing certain medical conditions and/or population risk cohorts?
  • What is the typical ROI of their care management programs and population health initiatives?
  • How can this information be used to support risk-based contracting with payers and other providers?

GE Healthcare Camden Group Presenters:

Marino_Dan.jpgDaniel Marino, MBA, MHA, Executive Vice President Mr. Marino is an executive vice president with GE Healthcare Camden Group with more than 25 years of experience in the healthcare field. Mr. Marino specializes in shaping strategic initiatives for healthcare organizations and senior healthcare leaders in key areas such as population health management, clinical integration, physician alignment, and health information technology. With a comprehensive background in all aspects of practice management and hospital/physician alignment, Mr. Marino is a nationally acknowledged innovator in the development of Accountable Care Organizations and clinical integration programs.

chopra2-110511-edited-239718-edited.jpgShaillee J. Chopra, PMP, Senior Manager Ms. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models.

 

DiLoreto.pngDavid DiLoreto, M.D., MBA Dr. DiLoreto, senior vice president at GE Healthcare Camden Group, is a physician-executive who is highly experienced in executive management, strategy and operations of healthcare delivery systems, and managed care companies. He has deep management expertise in community-based and academic health systems, large group medical practices, hospitals, and managed care organizations. His areas of specialty include clinical transformation, population health, business process improvement, leadership development, medical informatics, and data management and analytics.

GreenB1.pngRobert Green, MBA, FACHE, CHFP Mr. Green is a senior vice president and the practice lead for the Financial Operations and Transactions practice. He has more than 26 years of healthcare experience with 13 years of healthcare consulting experience and 13 years of provider-based financial, operational, and strategic experience among health systems, hospitals, medical groups, management services organizations, and physician hospital organizations.

 

To Register:

To register, simply click the button below, complete a short registration form, and press the "Cick to Register!" button. You will receive a confirming email. A second email will be sent the week of December 5th with webinar login/call-in instructions.

Please note: This webinar is intended for providers, provider organizations, and industry partners. Because of the proprietarty nature of the information shared during this webinar, independent consultants and consulting agencies will not be provided access to programming. GE Healthcare Camden Group reserves to the right to limit attendance at this event. 

Value Model, Webinar, Digital Health Analytics

Questions?

Please contact Matthew Smith at msmith@ge.com

Topics: Webinar, Daniel J. Marino, Shaillee Chopra, Digital Health Services and Data Analytics, Value Model, David DiLoreto, Robert Green

Utilizing Analytics to Measure Risk and Evaluate ROI in Your Organization's Value-Based Care Initiatives

Posted by Matthew Smith on Oct 4, 2016 2:06:19 PM

By Shaillee Chopra, PMP, Senior Manager, and Daniel J. Marino, MBA, MHA, Executive Vice President, GE Healthcare Camden Group

Data AnalyticsHealthcare organizations transitioning from fee-for-service to value-based models are making substantial investments in building technology and operational infrastructure to drive new services and workflows. Developing an effective and an efficient care delivery system from which to identify and drive profitability under risk arrangements remains of utmost importance. However, developing an analytics framework to support population health management, evaluate potential and expected returns from investment continues to be a struggle for many healthcare organizations.

Creating an analytics-based evaluation model enables healthcare organizations to quantify risks and evaluate viability and value-add of outcomes associated with various decisions. It also allows them to measure and track return on their investments in technology and resources associated with various programs aimed at managing health of populations they serve.

An Analytics-Based Value Model for Population Health

The framework for this model is grounded in measurement of utilization by place of service. It provides near real-time insight into “what works and what does not” in an operational environment. It also serves as a mechanism to ensure an organization’s positive position during re-contracting discussions with payers since it is supported with demonstrable value of delivering the right care to the right patient at optimal cost in the most appropriate setting. It serves as an information-based decision making model that enables the organization to make the transition to pay-for-value while preserving margins and without upsetting the apple cart of existing payer -provider relationships.

The Need for a Value Model Within an Organization’s Population Health Framework

A value model allows your organization to develop a deeper understanding around which variables drive outcomes that impact decisions about investments and resource allocation. For example, high-performing and value-based organizations are committed to improving quality and reducing avoidable utilization and costs. Cost reductions are a byproduct of the reduction in avoidable ER and acute inpatient utilization for individuals with chronic conditions. These costs are offset by an increase in “good utilization” such as increased PCP visits, wellness screenings, and pharmacy costs associated with medication adherence.  An analytics-based value model allows your organization to meaningfully sift through large amounts of data to identify and isolate important variables for future strategic success.

It allows for assessment of returns on investments made in various disease management intervention programs for at-risk, rising-risk and chronically ill population cohorts. It identifies assets and value levers that can be leveraged to prioritize and tweak the operational models for optimal returns.

Finally, it allows your organization to use analytics as a cornerstone for an innovative and data-driven approach to population health management.

Key Questions to Ask Within Your Healthcare Organization

Top performing healthcare organizations that are invested in developing their analytic capabilities begin with the end in mind and work from an analytics roadmap. Below are some of the key questions to consider to ensure you are achieving outcomes you desire without “boiling the ocean” and wasting valuable resources on programs that have minimally aligned outcomes:

  • What are the key questions you want to answer? Do you have 3-5 clearly defined use cases?
  • Accuracy, validity and credibility of your data: Do you know what data is needed? Do you have the right data? Is the data useable, i.e., accurate and credible? What data do you not have? Do you have a data acquisition strategy?
  • Analytics roadmap: Have you established a needs based analytics framework within your organization? Do you have right skillset and adequate tools to conduct analytics? After you perform analytics do you know how to interpret the results and make them actionable?
  • Driving actions: Do you have an actionable strategy for executing on the results? What are the actionable opportunities to execute against? How are you evaluating competing analytic priorities for resources and investment?
  • Monitoring impact and evaluate results: Have you implemented processes to track performance as part of your activation plan? Are you tracking both outcomes and the steps required to achieve the outcomes? Are you leveraging predictive modelling to consider varied “What if” scenarios to continually optimize the focus areas of your operational work plan?

Utilizing an analytics-based value model allows your healthcare organization to optimally invest resources and dollars towards operational programs that generate outcomes and value most alignment with strategic goals.

Value Model, Health Analytics


chopra2-110511-edited-239718-edited.jpgMs. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models. She may be reached at shaillee.chopra@ge.com.

 

Marino_Dan.jpgMr. Marino is an executive vice president with GE Healthcare Camden Group with more than 25 years of experience in the healthcare field. Mr. Marino specializes in shaping strategic initiatives for healthcare organizations and senior healthcare leaders in key areas such as population health management, clinical integration, physician alignment, and health information technology. With a comprehensive background in all aspects of practice management and hospital/physician alignment, Mr. Marino is a nationally acknowledged innovator in the development of Accountable Care Organizations and clinical integration programs. He may be reached at daniel.marino@ge.com.

Topics: Value-Based Care, Healthcare Analytics, Daniel J. Marino, Data Analytics, Shaillee Chopra, Digital Health Services and Data Analytics, Value Model

Top 10 Recommendations When Selecting an Analytics Platform For Your Healthcare Organization

Posted by Matthew Smith on Sep 1, 2016 9:03:38 AM

By Shaillee Chopra, PMP, Senior Manager, GE Healthcare Camden Group

website-analytics_1.gifThe transition to value-based care for our healthcare industry is paired with the navigation of challenges related to shrinking margins and changing payment models. Analytics is often touted as the solution that will uncover new insights, drive cost reductions, improve quality, and enable competitive differentiation. Though investment in healthcare analytics technologies has skyrocketed in the past few years (estimated at close to $20 billion), organizations still struggle to realize a tangible return on their investment.

Instead of a singular technology product, analytics should be treated as an integrated approach to create an information architecture that provides meaningful insights that drive actions. Rather than building around fancy dashboards and functionality the product offers, organizations should shift the focus to creating operational capabilities that vendors have to build against. The following are 10 key recommendations to keep in mind when selecting an analytics platform for your healthcare organization.

  1. Outline key operational capabilities that the vendor partner must enable for your organization. As you begin with the end in mind, consider outlining what are the key strategic goals your organization must accomplish within the next 12, 24, and 36 months. What are some of the key operational outcomes that must be achieved to realize these goals? What are key market conditions that would be influencers to the roadmap? A few examples are:  your organization aims to capture market share by offering competitive services and offerings; referral management and managing out of network utilization are areas of concern; or entering into value-based contracts is imminent or has already occurred.
  2. Define key operations focused use case scenarios. Clearly outline the key problems for which you want to solve and/or the new insights/trends you want to gather from the analytics platforms (e.g., trends in referral patterns) and ask the vendor to demonstrate their solutions that are specific to these needs. This allows you to shift away from general “sales demonstrations” to tailored, meaningful discussions on how the technology platform will address your needs and challenges. It also offers insight into what workflows and capabilities for which you have to plan within your organization to effectively realize the value from this investment.
  3. Utilize the information framework as a blueprint to drive the technology selection process. Consider building a data integration roadmap that outlines the data types that would be integrated into the analytics platform and in what sequence and frequency. The framework will identify how your organization can monitor and address variability when the quality of data is in doubt. This framework acts as a blueprint against which existing and new technology vendor partners in your portfolio have to deliver..
  4. Establish a multi-disciplinary selection council that conducts product assessment from varied perspectives (technical, clinical, financial, administrative etc.) Leverage the data governance framework to provide input and oversight for product selection so that there is a clear sense of how it will be operationalized.
  5. Develop a detailed product functionality assessment listing and a weighted scorecard that assist in objective assessment of product functionality. Create a detailed outline of product functionality that is critical to activation of operations use cases. This can include, but is not limited to, a detailed outline of reports, dashboards, work lists, business intelligence logic, need to integrate with other systems that will consume the data, etc. Consider developing a scorecard that will assist the selection team to objectively evaluate each feature.
  6. Collaborate with the vendor partner to develop a future state solution framework.
    Vendor SelectionSuccessful implementation of analytics tools and the ability to generate actionable information are highly dependent on effective integration with the current technology portfolio. Consider creating an architecture notebook outlining the current technology footprint of your organization, where relevant data pools exist, data needs, and limitations. Outline known data integrity concerns that the analytics vendor must respond to during demonstration sessions.
  7. Understand vendor data consumption capabilities and future roadmap. As your operational needs grow so will the need to consume new and varied types of data sources. Evaluate the varied data types (claims, payer, clinical) that the product can ingest today and the format and frequency of it. Does that align with your information architecture? Examine known limitations and mitigation plans. Consider the vendor’s plan for ingestion of additional data types such as patient provided data, device integration, pharmacy, retail etc.
  8. Review the vendor product and services roadmap to assess long term impact against your organization’s strategy. Being an emerging technology area that is constantly evolving, most vendors are still developing various functionalities. As a result, it is not uncommon that vendors end up over promising and under delivering. Carefully assess the vendor product and services roadmap and compare against your organization strategy. Does this offer an opportunity for a development partnership or at risk arrangements for shared commitment and success?
  9. Compare the vendor implementation approach against your operational needs. Most times analytics projects become too complicated too fast. Consider instead an incremental build approach that is problem focused. Evaluate if the products’ implementation approach allows for a phased build and is it focused on providing immediate value. Outline key external dependencies that impact speed to market. Evaluate if the proposed implementation roadmap allows you to position it as a value add service for your members and stakeholders within the first 60 to 90 days.
  10. Develop total cost of ownership ("TCO") model for the product to be implemented, integrated and operationalized within your organization. Additional cost factors can include data sources integration costs, infrastructure maintenance, organizational resource needs for implementation, governance and data integrity oversight etc. Be sure you have a complete picture of short and long-term costs to avoid troubling surprises when it’s too late.

As you approach technology selection with the mindset for creating organizational capabilities (e.g., services, outcomes, and experience) vs. building around functionality that the product offers you will be able to build an analytics framework that can objectively measure the value it provides and achieve progress towards the end goals you want to realize.

Analytics Platform


chopra2-110511-edited-239718-edited.jpgMs. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models. She may be reached at shaillee.chopra@ge.com.

 

Topics: Healthcare Data, Data Analytics, Shaillee Chopra, Digital Health Services and Data Analytics

5 Key Questions to Ask when Building an Analytics Framework for your Healthcare Organization

Posted by Matthew Smith on Jul 28, 2016 11:30:09 AM

By Shaillee Chopra, PMP, Senior Manager, GE Healthcare Camden Group

shutterstock_258948038.jpgAs the healthcare industry continues to make the shift towards value-based care models, organizations are faced with an increasing need to invest in technology platforms that offer analytics-based decision making. Faced with rapidly evolving strategic needs and surrounded by abundance of technology choices, healthcare executives struggle to conceptualize an effective approach to creating an analytics framework for their organization. Rather than adding one more product to the portfolio, organizations want to create an information framework that drives decisions and is action oriented.

Following are key strategic questions to ask when building an analytics platform for your organization. This approach ensures that you are able to successfully outline a data and analytics strategy that leverages adequate and accurate data across the network to create an action-oriented knowledge framework that is closely aligned with the value proposition.

1. What are the key problems you wish to solve?

Key to establishing an analytics framework that drives decision making and actions within the organization is to ensure that you start with the end in mind. What are some of the key objectives that your organization wants to achieve? Is facilitating transitions of care within the integrated network an immediate need? Is your organization aiming to capture market share by offering competitive services and offerings? Is referral management and reducing patient outmigration (often referred to as ‘patient leakage’) an area of concern? Is entering into value-based contracts one of the long-term goals for your organization? Succinctly outlining end goals guides what problems need to be solved for and helps conceptualize knowledge framework that will assist with the decision making.

2. What type of data is required to build this information framework and at what frequency?

Value-based goals and metrics often require harmonized data across the continuum. Atypical data types include, but are not limited to, billing data, inpatient EMR data, outpatient EMR data, ambulatory data from employed and affiliate provider practices, claims data from payors, HIE data, external pharmacies data, bedside monitoring, and at-home monitoring data.

Outline which data types are required to be updated in real time to support predictive analytics needs (clinical data elements such as diagnosis, problems, medications, etc.) and which ones can be brought in a retrospective format (claims, utilization etc.). This will assist you in establishing an integration architecture with various source systems across the network. Developing integration architecture also offers an opportunity to estimate realistic answers to how much the infrastructure will cost and how long will it take.

Consider building a data integration roadmap that outlines the sequence in which various data domains would be aggregated into the analytics platform. 

3. What is the data worth?

The relative importance of data from varied locations across a continuum of an integrated care network can influence your data acquisition strategy.  Key considerations include:

  • Data relevance. How relevant is the data type in your decision making process and what problems does it help solve for?
  • Data accuracy. Are all expected attributes of data accessible and transmittable? Is there minimal uncertainty due to workflow at the front lines?
  • Data frequency. Is the data available in a required format (discrete and structured vs. static image)? Is the data transmittable at a desired pace (updates to lab data might be required in real time to facilitate transitions of care; while claims data can be loaded in retrospectively to produce trends in cost-of-care)?
  • Data depth. Is there needed depth of historical data that is aligned with analytic needs (certain specialties such as oncology clinical historical data needs go farther back than primary care)?
  • Data consistency. Are the standardized vocabularies integrated into data domain offering consistent definition and interpretation of each data element?

4. What organizational capabilities need to be developed to support the future state?

It is important to identify key consumers of analytics within your organization early on. Understanding key information needs of the users, level of data literacy (ability to understand and interpret data) and the ability to exploit information offered via an analytics platform determines the pace at which your organization can adopt a knowledge-based decision making system.

Consider setting up a multidisciplinary data governance council that aims to provide:

  • Guidelines for management of the quality of data being leveraged across the continuum
  • Data literacy within consumers of analytics across the continuum
  • An operational framework that allows for maximizing data exploitation for the organization’s benefit

5. What are some key requirements for technology solutions that will aggregate and harmonize this data?

Product selection driven by clearly outlined end goals that the organization wants to achieve and key functional capabilities it wants to enable (care coordination, consumer engagement, increase market share, stop leakage) ensures that technology is successfully positioned as an enabler of operational workflows.

Key considerations when outlining requirements for an aggregation platform include:

  • Capabilities enablement. How does the technology platform operationalize key functional areas for the organization?
  • Product functionality. What are some of the key functionality needs to support operational objectives for the organization?
  • Data consumption abilities. What is the data footprint that the product can consume (clinical, financial, socio-demographic etc.)? What is the integration footprint with key healthcare technology vendors?
  • Product roadmap. What is the product roadmap, and how does it align with your organization’s strategic goals?
  • Speed to implementation. What is the product implementation methodology? What are the key resource needs from your organization, and what are key external dependencies that impact speed to market?

Transitioning to outcomes-based decision-making frameworks enables your healthcare organization to harness the power actionable analytics. You can leverage these best practice recommendations to avoid commonly observed pitfalls and implement a sustainable and scalable solution.

Digital Health, Advanced Analytics


Chopra.pngMs. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models. She may be reached at shaillee.chopra@ge.com

 

 

Topics: Analytics, Data Analytics, Digital Health Strategy, Information Framework, Shaillee Chopra

Top 10 Best Practices to Ensure Successful Analytics Implementations

Posted by Matthew Smith on Jun 30, 2016 11:52:42 AM

Many integrated care networks kickoff the infrastructure set-up with the implementation of a data aggregation and analytics platform. The decision is often fueled by the need to “do something” and backed by a loosely defined framework of questions that would have to be addressed. After an expensive financial investment and months of resource utilization, the outcomes often seem disappointing.

Organizations find that the product set-up further magnifies the gaps and cracks in the underlying “data pool.” It does not offer a knowledge-based information framework for decision-making, and the operational goals are not realized. They find themselves questioning the product selection and wonder what the next steps should be.

So why do so many of these analytics projects fail and why are so many executives and users still unhappy? While reasons for failure are varied, the biggest reason is that organizations miss out on the opportunity to create a well-defined information roadmap that is closely aligned with the operational goals. Instead of viewing data analytics as a product, it should be considered an organizational capability that grows and evolves iteratively. It is one that requires continuous alignment against operational goals, evolving market landscape, and changing technology vendor roadmaps.

Here are the top 10 best practices that will mitigate risks of failure and ensure successful analytics implementation within your healthcare organization.

1. Begin with the end in mind. The most common leap organizations make when addressing an operational need is jumping to a technology solution. Technology should be positioned as an enabler of the overall solution framework that is aligned with operational needs. Start with the end in mind. What are the key strategic goals your organization wants to achieve within 12, 24, and 36 months? What operational outcomes must be achieved to support those strategic goals? What are some of the key information needs that will assist with activation of the strategic roadmap? What are some of the key trends in the competitive market space that should be factored into the planning?

2. Build an information roadmap that serves as a blueprint for the organization and against which vendors must deliver. With target outcomes and capabilities in mind, build an information framework for the organization. The framework helps identify what type of data is required from sources (e.g., clinical, financial, socio-demographic, community, retail), what form the data needs to be pulled in (e.g., structured, unstructured, text, multimedia, data from devices), at what frequency the data needs to be updated (e.g., real time/dynamic or retrospective). The framework also helps define how the organization will address data variability when the quality and content are in doubt. This framework is an extremely valuable tool in helping understand the pace at which the organization can build capabilities such as care coordination, consumer engagement, support for entering into value-based contracting, referral management, and creating affinity to attract greater market share. Finally, it acts as a blueprint against which existing and new technology vendor partners have to deliver
 
3. Build for knowledge frameworks vs. bigger data lakes. Organizations should look to meet short-term needs but not lose sight of new capabilities that can be enabled by the information framework. Consider utilizing an operationally-driven used-case framework to structure a solution architecture that will allow you to uncover Knowledge_Framework.jpgpatterns previously hidden and find more complete answers. The information roadmap will help the organization harness the power of data and methodically turn it into applied knowledge.
 
4. Ensure that your implementation roadmap is focused on creating capabilities and not led by product functionality. As you architect the solution framework, maintain focus on creating new capabilities (e.g., services, outcomes, and experience) vs. being driven by technology product functionality. Often, operational workflow and end user experience are driven by the functionality of products. While within their silo they meet a particular operational need, without a context of an integrated information framework, they lead to fragmented and incomplete outcomes.
 
5. Create the focus on data quality rather than data quantity. The quality and relevance of information are infinitely more important than the amount of data aggregated in the system. Instead of focusing on the amount of data, focus on what the data reveals about trends in the care and outcomes. A crowded system can hide insightful and useful information. By recognizing data characteristics (form, type, value, and shelf life) organizations can build a meaningful data quality management framework and deploy the most appropriate data cleaning workflows.
 
6. Break down barriers and build bridges. Silos of technical solutions create a fragmented health view of the patient and operational workflow. It leads to gaps in care and a disjointed patient experience. Champion for an integrated information framework that requires technology vendors to support the desired inter-operability. The information roadmap and solution architecture are key tools that can be used to facilitate discussion and creative cohesiveness.
 
7. Create a multi-disciplinary data governance framework. A multi-disciplinary data governance framework will ensure that the analytics project is not an IT-driven initiative. It prevents a narrow perspective to tactical and strategic oversight for a component that is foundational to transition to value-based care models. Examples of areas in which the data governance committee assists include providing guidance on resolution of data integrity issues ensuring a single source of truth and establishing policies and procedures to govern data access and utilization.
 
8. Start small and build for scalability. Often analytics solutions become too complex too fast, yet lack the ability to produce information upon which to act. Information produced from an analytics platform might identify a problem, but an established operational framework is needed to solve the problem. The usability of the analytics model is closely tied with best practices, methodologies, organizational structures, and workflows that encompass it. Consider starting with a structured proof of concept that builds the foundational framework (operational and technology) and aims at cashing in on low hanging fruit and quick wins. Recognize that the analytics roadmap is not a one-time implementation. Consider agile process for scalability that allows for interactive input and expansion.
 
9. Build for adaptability to evolving strategic needs and competitive landscape. Quick wins via structured proof of concept allows for increased engagement with stakeholders. It will also highlight the gaps in collection of data at the front lines. Consider establishing a change management framework that helps manage the culture change in how the organization collects data, uses actionable information, and makes outcome-based decisions. Ensure that the information framework factors in data sets that you would want to tap into in the future (community data, social, etc.)
 
10. Build for sustainability by focusing on skillsets. Create a road map that allows you to gradually build the required skills within your organization. This will allow you to minimize risk, dependency on outside resources, and create ownership. As organizational maturity to consume complex analytical information increases, plan for future resource needs such as data scientists. Champion using the power for actionable information to create analytic consumers vs. application users within your organization.

Transitioning to knowledge and outcomes-based decision-making allows organizations to harness the power of actionable information into insights that create influence. The journey is filled with many learnings and opportunities. Organizations that can leverage best practice recommendations can avoid common pitfalls and ensure more immediate and sustained success.

Digital Health, Advanced Analytics


Chopra.pngMs. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models. She may be reached at shaillee.chopra@ge.com

Topics: Healthcare Data, Data Analytics, Data Governance, Outcomes-Based Decisions, Digial Health, Shaillee Chopra

Veterans Affairs Healthcare Summit: Transitioning to Personalized Health Services for Female Veterans

Posted by Matthew Smith on Jun 1, 2016 1:06:54 PM

By Shaillee Chopra, PMP, Senior Manager, GE Healthcare Camden Group

women-in-uniform-696x348.pngI recently participated in a panel discussion on women’s health services at the VA Summit. Female veterans comprise a small percentage (approximately ten percent) of the total veteran population in the U.S and Puerto Rico. However, utilization of healthcare services within the VA system by female veterans is projected to double by 2018. With this shift in demographic mix, the VA is championing the introduction of a wide-spectrum of women’s health services. The challenges are not limited to introducing new service lines, but also presenting them in an improved context of gender awareness and gender sensitivity.

As the VA embarks on this journey to improve female veteran access to one-stop shop, gender-sensitive, personalized, and comprehensive care, what are some of the best practices that can be leveraged to ensure success, scalability, and adaptability?

Begin With the End in Mind

The most common leap organizations make when addressing a critical need is jumping to a technology solution. Technology should be positioned as an enabler of the overall solution framework that is aligned with operational needs. Consider starting with the end in mind.  What new women's health service line does the VA want to enable? What are Data Governancethe considerations for access to this health care service? Are there specific socio-economic factors that characterize the target population that should be factored in? Are there unique patient experience needs that should be integrated into the care delivery framework (such as behavioral health assessments, common equipment use, PTSD triggers with equipment, etc.)? What are some of the key target outcomes that the VA desires to measure and strive towards?

Leverage What Is

Led by an operational framework of the future state, start with what information points are needed to operationalize that framework. Does the data need to be dynamic and real-time or retrospective? Are there current technology solutions (EMRs, specific women’s health services applications, equipment, etc.) that can be applied towards this new operational framework?

Maintain Focus on Creating Capabilities vs. Functionalities

As the overall framework for female veteran health services is designed, (inclusive of workflow, resources, culture, environment and technology) a focus on creating new capabilities (services and experience) must be maintained vs. being driven by technology product functionality. Often, operational workflow and end user experience are driven by the functionality of the best of breed products. While within their silo they meet a particular care need, without a context of an integrated information framework they contribute towards disjointed patient experiences and care team workflows.

Build for Scalability and Adaptability

Healthcare needs of veterans differ from the general patient population. Layer on top the gender-specific needs of female veterans—which are still being uncovered and understood. Female veterans may be at risk for PTSD and other mental health concerns; and their combat-related physical injuries can be very different from those of male veteran patients. As new delivery frameworks for female veteran services are launched, start small; build in adequate data and information points to observe success, risks, and failure points. Leverage the power of meaningful data and actionable information to drive scalability and adaptability.

Continue to Build Knowledge Framework vs. Bigger Data Lakes

VA_2.jpgOur healthcare organizations continue to have an abundance of discreet and disjointed data. This results in minimal information that is meaningful and can be acted upon. Furthermore, it creates a significant lack of applied knowledge that can drive informed and evidence-based decision-making. Consider building an information roadmap that will help the organization harness the power of data and methodically turn it into applied knowledge.

Break Down Barriers and Build Bridges

Silos of technical solutions create a fragmented view of the patient’s health. This leads to gaps in care and a disjointed patient experience. Champion for an integrated information framework that requires technology vendors to support the desired inter-operability.

Finally, it is all-in service to the informed, engaged and empowered female veteran who is at the heart of the evolving personalized care delivery model: As new care programs, new care protocols, improved facilities, and newer technology solutions are introduced, we need to consistently measure against the end goal-- do all these new offerings contribute towards, and create a seamless and personalized care for the female veteran in need?


Chopra.pngMs. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models. She may be reached at shaillee.chopra@ge.com

 

 

Topics: Care Delivery, Personalized Health Services, Womens Health, Veterans Affairs, Shaillee Chopra, Female Veterans

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