GE Healthcare Camden Group Insights Blog

Top 10 Considerations for an Optimal Data Science Strategy

Posted by Matthew Smith on Nov 2, 2016 1:59:35 PM

By Tony Ursitti, MS, Manager, GE Healthcare Camden Group

Data science is the field of techniques, tools and frameworks used to study and make meaningful conclusions from data. Data is being collected at an accelerated rate and the techniques, tools, and frameworks available to data scientists have evolved significantly over the last several years. The growth of this field has created tremendous opportunity as well as challenges for leaders in the healthcare industry. As this field evolves, healthcare leaders will need to become more knowledgeable about what investments are required in order to make the best use of their data.

There are ten key points for healthcare executives to consider when forming a data science strategy:

1. Realize that data is an asset, but its value is directly proportional to how it is used

Data is an asset to organizations in a similar way that buildings and medical devices are assets. An increasing share of the present and future value of health systems will be derived from the type of data that is collected and how effectively it is used to meet clinical, financial, operational, and strategic goals.

2. Understand the types of available data assets and prioritize the ones to acquire

As the pressure to cut costs and demonstrate high quality care continues to mount, understanding the types of data assets available and also the ones necessary to acquire is increasingly important. Consider how data assets are positioning the organization for future success…or future difficulty. For example, if a data element needed for calculating a quality metric that will be publicly reported in two years is not yet being captured, determine how quickly it can be captured. Two years from now, when that data becomes publicly reported, there will be no ability to go back and create a historical data record to use, and the organization could be understating the quality of care they provide simply because they don’t have the historical data to prove how well they actually performed.

3. Position your organization to compete on analytics

In the coming years, some organizations will thrive and others will struggle. Organizations that make smart use of data and analytics will have a strong competitive advantage over those that do not. Increasing pressure to cut costs and improve quality means increasing pressure to understand as quickly as possible the factors or organizational behavior that are contributing to positive as well as negative results.

4. Understand the importance of data science in getting the most from data assets

When planning to acquire the appropriate data assets, anticipate how the organization will make the best use of these assets once they have been acquired. Data science helps organizations start to understand some of the important relationships between practice patterns or other organizational behavior and undesired or desired outcomes. For example, it is important for organizations to know that their average length of stay at a particular hospital is increasing, but it is at least equally important to know why this increase is occurring and what, if anything, can be done about it. Data science helps uncover the why and enables organizations to make more informed strategic decisions.
One general principle for executives to keep in mind when thinking about data science is that good decisions require good knowledge, that good knowledge requires asking good questions of data, and that it is impossible to know what the “good questions” are unless you understand the scope of questions that are answerable. In other words, 30 years ago it would have been fruitless for executives to be asking questions like “what variables contribute to readmission risk and to what extent” because methods for answering that question did not exist in the way that they do today. Today, because of advances made in data science, that type of question is not only appropriate to ask but increasingly important to answer. Because data science has advanced so rapidly, the risk is no longer asking questions that cannot be adequately answered, but not understanding the breadth of questions that can be answered and, consequently, leaving them unasked.

5. Keep a focus on the critical problems

While starting or continuing on the journey of using the increasingly advanced techniques, tools, and frameworks that data science makes available, it is important to keep a focus on the problems that are most important for the organization. Data science is something like a Pandora’s box; insights beget insights and those insights beget more. Without the proper focus from leadership, data scientists will find endless interesting insights that have little or no strategic value for the organization. Good data scientists should be able to uncover insights buried in data that would otherwise be left undiscovered, but for this discovery to be relevant requires an appropriate level of understanding between leadership and data scientists about the most important strategic pain points the organization must address over the coming years.

6. There is not a “one size fits all” approach to data science

Each organization is at a different stage in the journey to deliver lower cost and high quality care while competing in local markets with different dynamics. Data insights that are of significant strategic value for one organization may be of little or no strategic value for another. For example, large multispecialty organizations can benefit from a robust site of service strategy that not only moves volume out of inpatient settings where appropriate, but also moves elective procedures from high cost inpatient facilities to lower cost inpatient facilities. For physician groups, the data and analysis requirements to build a site of service strategy would need to be more granular to be of any value.

7. Get the technical expertise required to succeed

Organizations will need access to technical expertise to help sort out the signal from the noise. Concepts like model accuracy, overfitting, and statistical significance will be important to understand in order to make sure that the inferences being drawn from modeling efforts are well founded.

8. Data science is not a “check the box” exercise

There are increasingly advanced techniques for making sense of data, and the organizational decision-making process must continue to evolve in order to keep up with and derive full benefit from these techniques. This means that the way organizations solved problems and developed strategies 5 to 10 years ago is no longer the best way to solve problems and develop strategy. As data science continues to advance, organizations must continue to evolve and make smart use of it.

9. Data literacy will be increasingly important at all levels of management

There is an increased need for data literacy within members of executive teams and across many operational areas within health systems. Good data scientists will be able to translate most of their technical knowledge into actionable insights for non-technical leaders. However, executives and other stakeholders will need to know enough to ask relevant questions about the data science with which they are presented to ensure nothing important is lost in translation. Iterative conversation between leadership and data scientists is incredibly powerful.

10. Competition for data scientists will increase

There is and will continue to be a shortage of data scientists in the market. In 2014, Accenture found that more than 90 percent of its clients planned to hire employees with data science expertise, but more than 40 percent cited a lack of talent as their number one problem. This problem is even greater in healthcare, an industry that is uniquely complex in which knowledge is highly specialized and takes years to develop. This means it is critical to start focusing now on building internal or external teams to support your organizational decision-making through data science.

Ursitti_Tony.pngMr. Ursitti is a manager with GE Healthcare Camden Group in the Digital Health and Advanced Analytics practice. He has more than seven years of analytics and leadership experience in both the consulting and provider settings. He focuses on helping health executives make data-driven strategic decisions through value model development using statistics, predictive modeling, advanced data mining, and machine learning techniques. He may be reached at


Topics: Data Analytics, Data Governance, Digital Health Services and Data Analytics, Data Science, Tony Ursitti

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

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

Top 10 Health IT and Analytics Considerations for Effective and Efficient Care Management

Posted by Matthew Smith on May 23, 2016 1:55:44 PM

The demand for meaningful and actionable data in healthcare has never been as prevalent as it is today. While most healthcare organizations are utilizing some form of health information technology (“health IT”) platforms to capture clinical documentation, system integration and broad spectrum analytic capabilities offered by these platforms are very underdeveloped. As pay-for-value becomes the standard for healthcare service reimbursement, organizations are beginning to realize the importance of establishing more efficient and effective care management processes that are supported by robust health IT systems and analytics frameworks. Below are 10 key health IT and analytic considerations to enable an effective care management program.

  1. Data Governance. Data is the crux of healthcare improvement. It is critical to establish a data governance council consisting of an interdisciplinary team that is responsible for developing a set of processes that serve as a quality control mechanism for handling information. These mechanisms define lines of responsibility and establish methods to foster the accessibility, completeness, and integrity of data. Strong data governance that can validate the accuracy of the data is critical to instill trust among clinicians. The absence of a data governance structure exposes the risk of clinicians contesting the accuracy and usefulness of the performance information produced by the data, thereby reducing its value to continuous improvement initiatives.
  2. Health IT Strategy. An organization must develop a health IT and analytics strategy to determine the types of health IT necessary to support the clinical and operational processes of the organization. The most essential component in the development of a successful health IT strategy is to gain consensus from all affected stakeholders. Attempting to implement a strategy without the buy-in from the affected stakeholders will be extremely difficult, if not impossible, because clinicians will not support the efforts and may challenge the types of health IT implemented.
  3. Systems and Technology Framework. The systems and technology framework of healthcare organizations is very complex. Organizations must inventory the various systems and develop a blueprint to guide system selection, technical and functional builds, consistent standards, and information output. All of these aspects affect clinical operations, and the implications must be considered and risks mitigated. Neglecting this activity will have serious implications on system integration and data aggregation efforts.
  4. System Integration and Solution Architecture. Healthcare organizations have various technology systems and platforms that were typically implemented at a point in time to serve a specific business need without extensive thought or planning given to system integration. The result of this approach is that these systems are not designed to “speak to each other”. Organizations must build an enterprise architectural approach that allows platform integration of multiple disparate systems, supports system interoperability, and establishes a centralized master data management structure.
  5. Solutions Search and Selection. Commonly, a systems and technology inventory reveals gaps in the framework, and the organization must embark on a search and selection process to identify a system to fulfill the need(s). It is important to establish a systematic approach to IT system selection so that the evaluation of the systems is comparative. More important, the organization must enlist the help of an interdisciplinary team to evaluate and score the systems based on a pre-defined list of functionality and standards. The process must be executed very methodically, starting with defining the system requirements, developing the evaluation criteria, training the evaluation team, conducting the system demonstrations, reviewing test cases, and selecting the system of choice. In addition, the organization must establish an enterprise approach to system search and selection, which can help minimize ad hoc purchases that can conflict with the overall integration strategy.
  6. Workflow Redesign. Clinical workflow redesign is integral with any health IT solution implementation. Before even embarking on system selection, an organization must first understand the clinical workflow. This will provide insight to the existing processes and protocols and enable the organization to select a technology solution that will best support clinical operations by incorporating solution sets that promote workflow redesign around functional roles, care model redesign, clinical decision support, and clinical protocols. Failure to consider the clinical implications when selecting a system will lead to an underutilized system and decrease efficiencies.
  7. Clinical Health IT Optimization. Clinical care optimization is critical to maximize the value of the health IT system. Health IT systems are rarely used to their fullest potential. This underutilization can have negative impacts on many facets of a healthcare organization, including patient safety, quality of care, clinical performance, staff satisfaction, and revenue capture. The organization can improve the functional use of systems and applications through utilization evaluation, reeducation of clinical documentation requirements, and system functionality retraining for clinical staff. Failure to optimize clinical health IT systems leaves the organization vulnerable and at risk for poor coordination of care, fragmented communication, performance penalties, staff turnover, and decreased revenue.
  8. Integrated Analytics and Reporting Strategy. An integrated analytics strategy is imperative to establish a streamlined approach to developing, managing, updating, and reporting performance measures. Many organizations are faced with various regulatory, accreditation, and quality program reporting requirements. Typically, report writers will build ad hoc reports according to the specifications requested, but report reviews reveal that many of these reports include the same metrics. Organizations must catalogue the performance measures contained in all reports. This accounting should include the details of the measure (e.g., numerator and denominator), discreet data needed to calculate the measure, the source system for the data, the purpose of the measure, the report measure owner (both requester and developer), the user(s) of the information, and the user  status (internal or external). This will help the organization to delineate the various reports, identify duplications, and establish consistency across the clinical analytics and reporting requirements.
  9. Clinical Informatics and Analytics. In line with the analytics and reporting strategy, an organization must define an approach and process to ensure that solution capabilities enable the collection of discreet clinical data that supports the development of reliable, action-oriented reports. An important component to developing a sound clinical informatics and analytics process is having an interdisciplinary team composed of IT, clinical, and report analyst representatives. This team composition will ensure that the information needed from the clinical team is addressed, the technical team can build it, and that it is structured so that analysts can easily generate the necessary reports.
  10. Actionable Data Analytics. Once the type of information that is needed for analytical reports is determined and the process by which this information will be captured is defined, an organization needs to determine the most effective way to present the information, to whom the information should be provided, and how frequently in a timely manner. Most important is designing reports that present data in a usable, action-oriented, and meaningful way. The way in which data is presented can make the difference between impactful care management and quality improvement results and futile care coordination efforts.

Topics: HIT, Health IT, Data Analytics, Workflow Redesign, Data Governance

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

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

Posted by Matthew Smith on Aug 6, 2015 10:37:35 AM

This new download from GE Healthcare Camden Group focuses on how successful organizations:

  • Identify current information technology solution needs and challenges
  • Categorize existing issues in capturing and aggregating data as well as translating this information into clinical workflows
  • Create the blueprint for an information management and data governance strategy
  • Document and prioritize steps related to the "future state" information technology solutions framework and management strategies needed to support current and future business needs.

To download the PDF document, please click the button below to access the download page.

Data Governance, Value-Based Care, Population Health

Topics: Value-Based Care, Population Health, HIT, HealthIT, Data Governance

Building the Data Governance Strategy for Effective Population Health Alliances

Posted by Matthew Smith on Aug 5, 2015 12:42:52 PM

By Tara Tesch, MHSA, Senior Manager, GE Healthcare Camden Group

This is the third of three articles in the Population Health Alliances series. The first article examined physician engagement strategies and detailed specific strategies that have proven successful for alliances. The second article focused on the value of true care redesign.

High-performing organizations possess robust information technology ("IT") infrastructure and associated tools to deliver, track, and document patient-centered, evidence-based care at the point of service and can disseminate actionable and meaningful data quickly and transparently. IT infrastructure implementation is an iterative process and rarely do organizations have a “fully baked” IT solution at the onset of implementation.

There is no single vendor that can provide a comprehensive data analytics solution to meet all needs (see graphic below) at this time.

 Future State CI Network Platform

Population Health, Data Governance

© The Camden Group 2015

In order to truly impact how care is delivered, end users must have actionable information in real time to support care redesign efforts. Providing patient-relevant decision support at the point of care can improve provider effectiveness in delivering appropriate and necessary interventions, furthering the organization’s goals of improving individual and population health. Too many organizations stall in developing their IT infrastructure by letting “great get in the way of good.” IT should support the care not drive it, therefore, systems and tools must translate and support care redesign. Too much data that is not well organized or analyzed can simply create confusion and cloud the necessary focus required to impact population health.

It is critical for population health alliances to have a well thought-out IT strategy and data management plan that will provide connectivity between members. The strategy should call for a means to collect the data, offer a robust tool to aggregate the data, and support reporting that will translate information into behavioral change and allow providers to more effectively communicate with and engage patients. The key factor for success: build your strategy beginning with the end in mind.

 Data Governance Strategy Build

© The Camden Group 2015

Success begins with the development of an information management and data governance strategy, which includes a data governance structure (who is going to own it, clean it, analyze it), organizational structure (what resources and types are required), and core data needs (reportable, transactional). An objective of the strategy is to take data and create meaningful information that leads to action-oriented knowledge. Out of the strategy, capabilities will be identified that drive interoperability and analytics requirements. These requirements should provide the criteria for selecting health information technology (“HIT”) that support the business and clinical needs of the alliance. Avoid buying the tool then trying to create a strategy around it; this will inevitably fail.

Defining Objectives

Designing the data strategy requires a sophisticated understanding of the alliance’s business and clinical objectives, clinical guidelines and care processes, and requirements of analytics to support these activities. First, define the end goal (outputs) such as care management or value-based contracting, and identify the data sources that will be used (i.e., EHR, claims, ADT, etc.). Next, determine how the data will be used to support the outputs; will it be reportable and retrospective (e.g., risk stratification, predictive modeling, scorecards) or transactional and action-oriented (e.g., point of care, gap closure, alerts, real time analysis to support decision-making).

To be successful, this planning process must include clinical/operational leadership (e.g., chief medical informatics officer, care management leads), in addition to finance and the member organization chief information officers. Staffing should include a data architect and a clinical informaticist able to translate the data into clinically meaningful information.

Once the strategy has been defined, identify the data requirements and associated capabilities. This may include standard processes and reporting templates – tools to automate the current state and optimize care delivery. Next, select a vendor that either has the ability to grow with your organization as it evolves or decide to pursue a “plug and play” vendor approach. Either way, the vendor must support the alliance’s CIN data requirements and capabilities.

In the end, it is critical to maintain strong, positive relationships with clinicians during the design and development of these key technology capabilities. Clinicians drive the clinical care of patients and care models to support the delivery of clinical protocols. Organizational and individual needs will evolve based upon initial successes and challenges, and clinicians will bring forth a multitude of suggested and needed changes after the initial “go live.” Technology is the tool to support the clinical requirements, and developing ongoing processes to solicit clinician feedback for continued improvement is an important contributor to long-term success.

Data Governance, Value-Based Care, Population Health

Ms. Tesch is a senior manager with GE Healthcare Camden Group in the clinical integration practice with more than 18 years of experience as a healthcare leader and strategist. Ms. Tesch specializes in value-based care delivery strategic planning, CIN development and implementation for commercial, Medicare, and Medicaid populations, health information technology data governance and analytics strategy, as well as care management strategy, design, and implementation. She has worked with a variety of healthcare providers, including integrated delivery networks, academic health centers, regional referral centers, rural community providers, and national non-profit and faith-based health systems. She may be reached at

Topics: Population Health, HIT, HealthIT, Data Analytics, Population Health Alliance, Tara Tesch, Data Governance

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