GE Healthcare Camden Group Insights Blog

Leveraging Data Analytics To Optimize Supply Availability In The Operating Room

Posted by Matthew Smith on Aug 29, 2017 6:12:44 PM

By Don Martin, Senior Manager, GE Healthcare Partners

Imagine you are the OR nurse or surgical technician setting up the sterile field for a procedure when you discover that a critical supply or instrument is missing from the case cart. You scan the physician procedure card and see the item is listed but for some reason has not been picked. The patient is lying on the table and the surgeon will be walking into the room at any time – and she will not be happy to learn that the case will be delayed or possibly postponed while you and others scramble to locate the missing piece. Most of you don’t have to imagine this scenario because unfortunately we’ve all had this experience more times than we’d care to admit.

We all know that the apparently simple task of having the right supply or instrument available when and where it is needed for a surgical procedure is in fact not simple at all. It often involves many tasks performed by multiple staff across several departments, including vendors, materials management, central sterile services and the OR staff itself. We also know that without the ability to forecast the demand for these resources and match that demand against availability, we are leaving it largely to chance that the resource will be available when needed in the operating room. Leveraging data analytics enables hospitals to optimize scheduling and resources in the operating room.

Case Study: Addressing Gaps In Perioperative Automated Supply Inventory Management

Recently, we partnered with a multi-hospital, integrated delivery system to implement a point-of-use automated supply inventory management in the surgery suites of three of its facilities. This project was one element of an overall management initiative to improve supply service delivery to Perioperative Services, with these key objectives:

  • Ensure supply items are available to the operating room staff when and where they were needed
  • Streamline supply throughput processes in the operating room
  • Reassign day-to-day supply chain management responsibilities in the OR from clinicians to supply chain staff, enabling the OR staff to focus exclusively on their clinical responsibilities

While the implementation project was successful from a technical standpoint, end-users soon learned that simply having procedure scheduling and supply inventory data readily available from their respective systems was not particularly useful unless that information was somehow integrated to provide forecasting data, enabling the OR and Materials Management staff to anticipate and act upon identified inventory shortfalls and changes in scheduling volumes. Consequently, the OR staff continued to rely on tribal knowledge to forecast supply demand, often with poor results.

Seeing this, management organized a workgroup composed of managers and key staff from the OR, Central Sterile Services and Materials Management departments to further understand this information need and design a platform to provide the desired forecasting data. The group learned that neither the surgery or point-of-use inventory management systems were capable of communicating directly with each other to produce the required forecasting reporting. They decided to focus their efforts to develop a tool that would assimilate and organize scheduling and inventory data from their respective systems to produce and distribute daily supply forecasting information to the operating room staff.

The team selected the Microsoft Access database application as the platform for generating the inventory data analytics. Extracts were created to source scheduling and inventory updates from the surgery information and point-of-use supply inventory management systems and import them into the Access database. Queries and reports were designed and built and, following a period of application testing and validation with an end-user pilot group, the application was installed on departmental workstations and distributed for use by the operating room and materials management staff. An image of the application’s menu directory is shown in the graphic below.

novia chart2.jpg

Data Analytics Improves Inventory Accuracy And Availability

Data analytics generated from this tool fulfilled two highly desired and critical reporting requirements:

  • Daily reporting of anticipated inventory shortfalls impacting cases the following day enabled the Materials Management staff to notify and work with the OR staff to secure supplies from alternate sources or identify appropriate product substitutes
  • Analytics of supply demand trending data compiled over time enabled the materials management staff to adjust Periodic Automatic Replenishment (PAR) and inventory refill points to achieve more stable and reliable inventory levels, reducing the spikes in inventory levels prevalent before the availability of this information.

As the OR staff began to gain confidence in the data analytics, they relied less on tribal knowledge and increasingly on the information provided by the analytic tool. With that, we also saw a reduction in behaviors such as supply hoarding, which have a negative impact on supply availability and inventory level accuracy. The graph below represents the OR supply fill rate following implementation of the analytic tool. The results from Day 1 through Day 10 reflect the general volatility of supply availability levels prior to the use of the tool. As the OR and Materials Management staffs gained proficiency with the tool and use of the forecasting data, fill rates were improved and stabilized in Days 13 through 28, with one exception. Please note that the fill rate measures shown below were taken over a three-month period.

novia line 1.jpg

Lessons Learned

As with any system implementation project, our team came away with several lessons learned:

  • Before embarking on this type of project, it’s important to assess the availability and status of clinical and operational requirements within the organization; we’ve consolidated these requirements into a Data Modeling Checklist to help other hospitals and systems
  • A multi-disciplinary project steering committee was essential to the success of the project in establishing analytic data priorities and maintaining the development team’s focus
  • Begin with small, focused initiatives to shorten the development and product delivery cycles and build rapid team competency
  • Early operational successes resulting from the use of the analytic data fosters staff confidence and reduces reliance on group tribal knowledge
  • As with the inventory forecasting data, future data analytic development initiatives must demonstrate clear quality, operational and/or financial value to gain approval for development

Non-Labor Expense Reduction

MartinD.jpgMr. Martin is a senior manager with GE Healthcare Partners with more than 20 years of financial and clinical experience with operational responsibilities for patient care delivery, fiscal management, staff development and government, and regulatory compliance. His collaborative approach guides clients through the complex process of optimizing existing technology to meet healthcare’s Triple Aim: increase operational efficiency, improve the quality of patient care, and decrease the costs of care.


Topics: Data Analytics, Perioperative Services, Supply Chain Management, Operating Room

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

Two New Downloads: Analytics-Based Value Model | Health System Integration

Posted by Matthew Smith on Oct 8, 2016 10:16:45 AM

Two new downloads from GE Healthcare Camden Group are now available for download. Simply click the button below each description, and you will be taken to the respective download page.

Leveraging an Analytics-Based Value Model to Optimize Healthcare Strategies

Healthcare organizations are faced with the challenge of measuring optimal performance in a complex environment. An analytics-based value model can provide decision-making capabilities to measure performance, risk, and evaluate return on investments.

This new download from GE Healthcare Camden Group details the importance of creating an effective analytics-based value model and focuses on:

  • Key components of an analytics-based value model
  • Key drivers behind value modeling
  • Sample value drivers and KPIs
  • Benefits of leveraging advanced data mining
  • Relationships between trends and outcomes
  • Components of risk stratification
  • Barriers to success

Value Model, Health Analytics

Health System Integration: You Need a Plan!

Health systems fail to achieve full integration due to a lack of effective planning, poor management collaboration, or subpar implementation. Regardless of the reason, a sound integration plan focused on the goals of the system and dedicated true integration will increase the odds of success.  

This new download from GE Healthcare Camden Group details the importance of creating an effective integration plan and focuses on:

  • The increase in the number of hospitals in mergers and acquisitions
  • Health system efficiency imperatives
  • Definition and applications of a business plan of operational efficiencies ("BPOE")
  • Individual hospital vs. system-based savings
  • System integration efficiency strategies

Health System Integration

Topics: Download, Data Analytics, Hospital Integration, Value Model

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


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

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

The 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


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

As 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



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

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

New Download: Digital Health Services and Advanced Analytics

Posted by Matthew Smith on Jun 15, 2016 12:51:33 PM

Are you unsure of how to start the process of building a data strategy and an information roadmap? Are you worried that you're not aggregating the right data? Are you stuck in neutral and not making any headway with your population health analytics vendor? 

If so, GE Healthcare Camden Group can start you on the right path.

Start here...with our PDF outlining ourDigital Health Services and Advanced Analytics practice.

The PDF includes pages on:

  • Our Philosophy and Approach
  • Reasons Why Healthcare Analytics Vendor Implementations Fail
  • Initial Questions from Leadership and Teams
  • Data Analytics Strategy Components
  • Information Services and Advanced Analytics

Simply click the button below to get started!

Digital Health, Advanced Analytics

Topics: EHR, EMR, Value-Based Care, Data Analytics, Digital Health Strategy, Digital Health Services and Data Analytics

Meet the Practice: Digital Health Services and Advanced Analytics

Posted by Matthew Smith on Jun 14, 2016 1:45:24 PM

This Meet the Practice overview, examining the new Digital Health Services and Advanced Analytics practice, is part of an ongoing series in which GE Healthcare Camden Group's senior leaders share insights into our six practice areas.

Practice Lead: Daniel J. Marino, Executive Vice President

Explain the needs and problems you solve for clients through this practice.

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. In order to ensure that intelligible, actionable information is created, organizations are creating digital health strategies that focus on producing value-driven analytics while supporting their operational capabilities and needs.

As a result, there is a need to assist healthcare provider organizations in:

  • Building an operationally-focused information services and analytic strategy
  • Provide direction in creating comprehensive and powerful advanced analytics
  • Incorporating enterprise-wide data governance integrating clinical, financial and technical master data management
  • Focusing on analytic deliverables and capabilities and less on application functionality
  • Improving integration of applications and technology with provider workflows and care model delivery
  • Maximizing the optimal use of existing applications
  • Providing objective, vendor agnostic professional advisory services to provider organizations

Ultimately, we enable clinically integrated organizations to maximize value through an operationally driven digital health strategy.


What is the value or ROI that is provided by solving these challenges?

Organizations with a comprehensive digital health and analytics roadmap will be able to realize benefits in strategic development, operational optimization, and performance effectiveness.

From a strategic development point-of-view, organizations will be able to expand their provider networks through enhanced connectivity, integrated care management, analytics, and value-based programs. Operationally, they will benefit from real-time performance data which supports operational output for clinical, financial, and contracting objectives. They will also see improved performance effectiveness via cost-of-care modeling to support value-based contracting, and improved outcome tracking and clinical program effectiveness measures.

What types of organizations need your services?

We engage provider organizations at various stages of digital preparedness. Many organizations are simply looking for a starting point in working with their population health analytics vendor and want to ensure that they have a results-oriented digital health development blueprint and are aggregating the right data that leads to meaningful information. Other organizations are looking to create a complex data strategy and roadmap amidst a sea of ever-changing priorities and information requirements.

What synergies differentiate this practice area (and GE Healthcare Camden Group)?

Within GE Healthcare Camden Group, our Digital Health Services and Advanced Analytics practice provides a 360 degree operational perspective to the information services and analytic challenges existing within healthcare organizations.

Because we are vendor agnostic, our objective experts understand the clinical and operational impact of the ever-changing technology landscape and are equipped to advise on IT/IS strategies, system selection, activation support, and advanced analytics.

As a firm, GE Healthcare Camden Group provides professional advisory services across the care continuum incorporating our expertise in information services and analytics. We have built a reputation as a trusted partner to clinical, operational, and financial healthcare leaders by helping them close the gap between their challenges and the optimal solutions for success.

To learn more about GE Healthcare Camden Group's Digital Health Services and Advanced Analytics practice, please click the button below for a PDF download.

Digital Health, Advanced Analytics

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

Digital Health Services and Advanced Analytics

Topics: Value-Based Care, Daniel J. Marino, Data Analytics, Digial Health, Digital Health Strategy, Digital Health Services and Data Analytics

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

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