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GE Healthcare Camden Group Insights Blog

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

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

Preparing for Value-Based Contracting? Here are 5 Key Questions for Providers to Consider

Posted by Matthew Smith on Jun 7, 2016 2:27:10 PM

By Sabrina Burnett, Vice President, GE Healthcare Camden Group

contracting.pngIn January, 2015, the U.S. Department of Health and Human Services ("HHS") announced it had set a clear goal and timeline for transitioning Medicare reimbursements from volume to value. HHS’s goal is to tie 30 percent of all Medicare payments to quality and cost performance by the end of 2016 and increasing that proportion to 50 percent by the end of 2018. Commercial payers are expected to follow suit and develop their own programs for basing payment on quality or value. Immediately following the announcement from HHS, several major health systems and the nation’s top health insurers announced the creation of the Health Care Transformation Task Force and challenged other providers and payers to commit to their goal of putting 75 percent of business into value-based arrangements by 2020¹.

It is no longer a matter of “if” we are moving to value, but “when." As providers prepare for impending change in reimbursement they must decide if they are going to lead, follow or resist.

5 Considerations

Regardless if providers are planning to engage in shared savings, accountable care organizations, bundled payments, or risk contracts, there are several questions to consider when developing their strategy: 

  1. Are you prepared to participate in value-based initiatives?
  2. Do you understand the healthcare needs in your market?
  3. Have you optimized the technology necessary to support success?
  4. Have you undergone care delivery transformation?
  5. Do you understand the financial implication of new reimbursement methods?

It is imperative to understand the clinical and technical capabilities, resources, and skills necessary to be successful in a value-based world. Conduct an organizational self-assessment to help identify current capabilities and those still needed to achieve status as a value provider. Additionally, catalog the existing payers, products, and value-based programs and know the health care needs in your market and the corresponding services offered. Knowing your capabilities and understanding your market serves as a blueprint or roadmap to develop your strategy and guide successful contracting efforts. As you begin to build the payer strategy, it is important to stay focused on what infrastructure/tools you need to possess or build to effectively manage risk while reducing costs of care and maintaining high-quality outcomes. 

Analytics Drive Positioning

To properly position for value-based care, providers must have progressive financial and clinical data analytics and reporting capabilities. Significant investments in technology and care model redesign are required to improve clinical quality, reduce inefficiencies, improve provider/patient engagement, and optimize financial performance. Payers have massive amounts of data and an infrastructure to collect, aggregate, and analyze the data. Look for payers that are willing to collaborate and work in partnership to leverage the data and infrastructure.

It is equally important to invest the time to understand the financial implications of the new reimbursement methods and the extent to which costs must be reduced and where, how individual participants will be incentivized to achieve these objectives and the short-term investment in the infrastructure required to produce improvements in care coordination and quality.

There is not a one-size fits all strategy, so it is important to know the provider’s readiness to participate in value-based initiatives, the different care delivery needs of the community, the scope of risk to be taken, and have relationships with payers and other providers that will be necessary to achieve success.

1. Source: “Major Healthcare Players Unite to Accelerate Transformation of U.S. Healthcare System” Health Care Transformation Task Force, June. 28, 2015.


sabrina_burnett1.pngMs. Burnett is a vice president with GE Healthcare Camden Group. With 20 years of professional experience, Ms. Burnett delivers a wealth of skilled leadership in health management processes and solutions-based planning and execution. She has in-depth knowledge of the post-acute industry and a thorough understanding of the healthcare market, payer reimbursement methodologies, including managed care requirements and strategies, and knowledge of relevant state and federal regulations and actions. Ms. Burnett is recognized for her expertise in contract negotiations, solutions-based planning and post-acute integration within the continuum of care. She may be reached at sabrina.burnett@ge.com

Topics: Value-Based Contracting, Healthcare Data, Provider Contracting, Sabrina Burnett, Healthcare Data Analytics, Volume-to-Value

New Download: Building the Information Management and Data Governance Strategy for Value-Based Care

Posted by Matthew Smith on Apr 27, 2016 2:12:00 PM

DownloadThe U.S. healthcare system is experiencing significant shifts in the way healthcare is accessed and delivered, fueling a strong desire for operational efficiencies, value-driven outcomes, and action-oriented information. 

This new 15-page download from GE Healthcare Camden Group focuses on how successful organizations transitioning to value-based care delivery systems can:  

  • Define strategic and operational goals that an integrated care delivery systems needs to address
  • Identify key challenges organizations face when defining a roadmap for an analytics strategy
  • Outline components of an action-oriented information systems framework for an integrated care delivery system
  • Define steps to create the information architecture to support a data governance strategy

To download this document, please click on the button below.

Data Governance

Topics: Value-Based Care, Health Information Exchange, Healthcare Data, Data Governance, Information Management

New Download: The Data Infrastructure to Deliver Accountable Care

Posted by Matthew Smith on Jul 10, 2014 3:42:00 PM

Data, Accountable Care, ACOIt could be said that data is the fuel and HIT systems are the pipeline for Accountable Care. Building the data infrastructure fo an ACO is a complex undertaking with considerable upfront investment.

Provider organizations must build a strategy, manage their cost structures, and understand the accountable care functions and IT systems that are necessary to make ACOs function properly.

This new PDF presentation from Health Directions examines the process for building the data infrastructure to deliver accountable care, including:

  • The importance of a data-enabled ACO
  • Claims vs. clinical data
  • Why clinical integration is necessary
  • Keys to creating a quality clinical integration infrastructure
  • Basic functions for accountable care
  • Associated IT systems
  • Building vs. buying

Simply click the button, below, to access the download.

  Data Analytics, ACO, Healthcare Data

Topics: Accountable Care, ACO, Healthcare Data, Data Analytics

Infographic: A History of Healthcare Informatics

Posted by Matthew Smith on Jul 10, 2014 10:33:00 AM

Healthcare Data Analytics, InfographicThe notion of healthcare informatics began in 1949 and rapidly evolved over the next 65 years. In 2013, nearly 75% of physicians used tablets to maintain electronic health records (EHRs), according to a new infographic from Adelphi University.

This infographic chronicles the history of healthcare informatics and details the road to healthcare digitization. To view a full-size image, please click here.

Healthcare analytics, data analytics, infographic

Topics: EHR, EMR, Infographic, Electronic Health Record, Electronic Medical Record, Big Data, HIE, Healthcare Informatics, Healthcare Data, Data Analytics

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