Deloitte: 2015 Survey of U.S. Hospitals and Health Systems on Analytics Investment and Strategy
Oct 12, 2015 08:00
CST Updated
08:00
“Analytics” and “big data” are currently hot topics in the healthcare industry. Many stakeholders agree that the insights provided by analytics can help organizations improve quality and reduce costs. The combination of these two elements is precisely a prerequisite for implementing effective value-based care (VBC).
As healthcare systems continue to face shrinking margins, tighter budgets, and evolving payment models, “analytics” is regarded as a key to unlocking new sources of value. But will its adoption and investment meet expectations?
According to Deloitte’s Center for Health Solutions’ “2015 Survey of Analytics in U.S. Hospitals and Health Systems,” healthcare systems’ spending on analytics aligns with successful analytics use cases. Respondents also unanimously agreed that investment in analytics is critical to value-based care (VBC). Nevertheless, many organizations still lack clear strategies, effective data management frameworks, and robust budgeting models.
This survey targeted 50 chief information officers (CIOs), chief medical informatics officers (CMIOs), and senior technology leaders from U.S. healthcare systems, academic medical centers (AMCs), and large private hospitals (with annual revenues exceeding $500 million). The purpose of the survey was to understand these institutions’ investments in and priorities for “analytics.”
Survey shows...
Adoption of and investment in “analytics” within the healthcare system are expected to grow, although the magnitude and pace of this growth may not be as pronounced or rapid as some industry insiders have predicted. In the survey, only five healthcare organizations indicated that they anticipate a significant increase in their “analytics” spending over the next three years. Furthermore, respondents from several organizations stated that they were unclear about their current “analytics” expenditures, making it difficult to determine future trends.
More than half of the respondents believe that the biggest obstacles to adopting “analytics” include culture, operational models, and fragmented regulation. Other factors may also include: lack of available funding; an excessive number of products offered by different vendors, which can cause confusion; and inconsistent definitions of “analytics” within the industry.
The transition from the fee-for-service (FFS) model to value-based care (VBC) is ongoing, with Medicare plans set to increase value-based payments in 2018. In this context, healthcare enterprises need to integrate financial, operational, clinical, and other data to achieve objectives such as improving quality, delivering accessible services, controlling costs, and managing provider networks.
Fragmented “analytics” strategies will fail to support the effective integration of data. While some leading organizations have recognized the importance of coordinated business models and sufficient investment in “analytics,” other enterprises are still struggling at the starting line, uncertain about the path ahead.
“Analytics” is rapidly becoming a competitive advantage in value-based care (VBC), adding value to various objectives of healthcare organizations, including consumer experience, revenue growth initiatives, and cost reduction. Healthcare organizations with centralized strategies and management structures are most likely to achieve superior performance through “analytics.”
Key Findings of the Investigation
This survey reveals that although “analytics” can drive VBC payment and delivery models, health systems are still in the process of defining their enterprise-level “analytics” strategies and investments.
Investment Drivers: Value-Based Care (VBC) is the primary driver and investment focus for “analytics.” More than 75% of respondents indicated that clinical efficacy, population health, and cost efficiency are the current top drivers for “analytics” investments (Figure 1). Respondents expect this trend to continue over the next three years, with future drivers of “analytics” investments including support for novel payment models, pursuit of annual revenue growth, fostering innovation, and enhancing consumer experience.
The focus of “analytics” investments reflects the priorities of value-based care (VBC), with clinical and population health emerging as the largest areas of “analytics” investment (Figure 2). Although financial management “analytics” was once a priority in healthcare systems, respondents indicated that population health and clinical “analytics” will take its place in the coming year.
Function: The current “Analytics” function remains limited, yet advanced analytics are an urgent priority for achieving Value-Based Care (VBC).
Healthcare organizations are frequently asked about the maturity of their current “analytics” capabilities and the key priorities for future development in this area. Generally, the scope of “analytics” ranges from business intelligence (e.g., reporting, dashboards, and ad-hoc queries) and relational data warehousing functionalities to predictive and advanced analytics capabilities (e.g., data mining, self-service analytics, statistical analysis, predictive modeling, natural language processing, machine learning, big data, and cognitive computing).
Many surveyed healthcare organizations already possess reporting capabilities for multiple business functions. Popular features that healthcare organizations plan to invest more heavily in building over the next three years include business intelligence and advanced analytics (Figure 3). Academic medical centers and health systems with annual revenues exceeding $2 billion believe they already have advanced analytics capabilities. However, survey results indicate that academic medical centers show weaknesses in predictive analytics and market intelligence capabilities (data not shown).
Respondents plan to prioritize the addition of advanced analytics capabilities in clinical care, population health, and financial management over the next three years (Figure 4), indicating that value-based care (VBC) is a strategic priority for their business development.
Costs: For many healthcare organizations, determining the total cost of "analytics" is quite difficult; there are signs that "analytics" costs are rising.
Fifteen of the 50 surveyed institutions stated that they do not track their overall spending on “analytics,” indicating potential fragmentation and lack of coordination in current “analytics” capabilities (Figure 5). Seven healthcare organizations claimed that their “analytics” budget accounts for 0% of their current operational budget; this may be partly due to the broad definition of “analytics.” The survey also revealed signs of growing expenditure on “analytics”: five of the surveyed healthcare organizations reported that their “analytics” budget currently represents 1%-5% of their operational budget, with projections to reach 6%-10% within the next three years.
Compared with non-academic medical centers and health systems with annual revenues of $2 billion or less, academic medical centers and health systems with annual revenues exceeding $2 billion are more likely to have higher “analytics” budgets (data not shown).
It is important that the level of investment in “analytics” aligns with the likelihood of achieving success. Healthcare organizations that allocate 1%–5% of their operational budgets to “analytics” have achieved success in certain functional areas (Figure 6). This may be partly attributable to the maturity of their analytics initiatives and the amount invested.
For example, over the past few years, many healthcare organizations have invested in electronic medical records (EMR), annual revenue cycle management (RCM), and enterprise resource planning (ERP) solutions, often accompanied by investments in business intelligence and basic analytics capabilities. Compared to other functional areas, these healthcare organizations reported greater success in financial management, clinical management, and enterprise performance management. A small number of respondents also noted improvements in population health management, though this is more likely attributable to their earlier adoption of value-based care (VBC).
Success: Healthcare systems with more mature analytical capabilities are more likely to achieve business success.
Healthcare systems that leverage “analytics” for more mature applications (such as advanced analytics and forecasting) are more likely to succeed (Figure 7). The more successful enterprises become through “analytics,” the more likely they are to invest in other advanced analytics solutions, thereby deriving further benefits and perpetuating this virtuous cycle.
Data Management Model: The “analytical” management model is fragmented, inconsistent, and variable.
Robust management models can help healthcare systems focus on priorities and strive to derive value from analytical insights. Although the surveyed healthcare organizations exhibited certain elements of strong data management strategies, many still lacked a centralized management model (Figure 8). Only 20 healthcare organizations had a clear and comprehensive strategic deployment for analytics across various business functions, although healthcare organizations with annual revenues exceeding $2 billion were more likely to claim having a comprehensive strategy (data not shown).
Of the 50 respondents, 21 stated that they lacked a formal enterprise-level data management process; only six had a Chief Analytics Officer (data not shown). However, three-quarters of healthcare organizations have established a department dedicated to providing enterprise-wide analytics (data not shown).
Nearly half of the surveyed organizations lack centralized oversight of “analytics” tasks and capabilities (Figure 9). The management of “analytics” architecture is often driven by IT organizations, while other functional “analytics” applications, such as workforce utilization, budgeting, and planning, are more likely to be driven by functional business departments.
Barriers to Adopting “Analytics”: Cultural Differences and Fragmented Ownership Are the Greatest Challenges
Culture, decentralized ownership, and access to technical resources are the biggest challenges facing the adoption of “analytics” in healthcare systems (Figure 10). Data access is the least significant barrier, but nearly half of respondents indicated that data quality is also an obstacle. For smaller healthcare organizations (with annual revenues between $50 million and $100 million), acquiring technical resources and securing corporate funding for “analytics” remain significant barriers to their adoption of “analytics” (data not shown).
Insights
Survey results indicate a positive correlation between the maturity of healthcare systems’ “analytics” initiatives and their success. Conversely, organizations lacking an enterprise-level “analytics” strategy, structured data management practices, and coordinated “analytics” investments are less likely to achieve comparable levels of success through analytics.
Healthcare organizations will require more sophisticated tools and capabilities to integrate, analyze, and leverage their data, thereby achieving Value-Based Care (VBC). The following recommendations can help healthcare organizations adopt “analytics”:
• Cultivate leaders committed to understanding and leveraging “analytics” to enhance corporate performance.
• Implement a structured data management model and an enterprise-wide “analytics” strategy.
• Manage “Analytics” capabilities and investments to drive innovation in functional business units and projects, enhancing their tangible value.
• Emphasize data and technical standards to promote interoperability and enable more effective utilization of “analytics” resources.
• Recognize the cultural dimensions of leveraging “analytics” to accelerate performance drivers.
Note: 1. The term “healthcare system” in this text refers to healthcare systems, academic medical centers (AMCs), and large hospitals.
2. “Analytics” refers to the systematic use of technologies, methods, and data to gain insights, enabling decision-making, planning, management, operations, measurement, and learning based on actual conditions.
Compiled by Chen Xin
Editor: Mo Renying
Download the Original English Version
As healthcare systems continue to face shrinking margins, tighter budgets, and evolving payment models, “analytics” is regarded as a key to unlocking new sources of value. But will its adoption and investment meet expectations?
According to Deloitte’s Center for Health Solutions’ “2015 Survey of Analytics in U.S. Hospitals and Health Systems,” healthcare systems’ spending on analytics aligns with successful analytics use cases. Respondents also unanimously agreed that investment in analytics is critical to value-based care (VBC). Nevertheless, many organizations still lack clear strategies, effective data management frameworks, and robust budgeting models.
This survey targeted 50 chief information officers (CIOs), chief medical informatics officers (CMIOs), and senior technology leaders from U.S. healthcare systems, academic medical centers (AMCs), and large private hospitals (with annual revenues exceeding $500 million). The purpose of the survey was to understand these institutions’ investments in and priorities for “analytics.”
Survey shows...
Adoption of and investment in “analytics” within the healthcare system are expected to grow, although the magnitude and pace of this growth may not be as pronounced or rapid as some industry insiders have predicted. In the survey, only five healthcare organizations indicated that they anticipate a significant increase in their “analytics” spending over the next three years. Furthermore, respondents from several organizations stated that they were unclear about their current “analytics” expenditures, making it difficult to determine future trends.
More than half of the respondents believe that the biggest obstacles to adopting “analytics” include culture, operational models, and fragmented regulation. Other factors may also include: lack of available funding; an excessive number of products offered by different vendors, which can cause confusion; and inconsistent definitions of “analytics” within the industry.
The transition from the fee-for-service (FFS) model to value-based care (VBC) is ongoing, with Medicare plans set to increase value-based payments in 2018. In this context, healthcare enterprises need to integrate financial, operational, clinical, and other data to achieve objectives such as improving quality, delivering accessible services, controlling costs, and managing provider networks.
Fragmented “analytics” strategies will fail to support the effective integration of data. While some leading organizations have recognized the importance of coordinated business models and sufficient investment in “analytics,” other enterprises are still struggling at the starting line, uncertain about the path ahead.
“Analytics” is rapidly becoming a competitive advantage in value-based care (VBC), adding value to various objectives of healthcare organizations, including consumer experience, revenue growth initiatives, and cost reduction. Healthcare organizations with centralized strategies and management structures are most likely to achieve superior performance through “analytics.”
Key Findings of the Investigation
This survey reveals that although “analytics” can drive VBC payment and delivery models, health systems are still in the process of defining their enterprise-level “analytics” strategies and investments.
Investment Drivers: Value-Based Care (VBC) is the primary driver and investment focus for “analytics.” More than 75% of respondents indicated that clinical efficacy, population health, and cost efficiency are the current top drivers for “analytics” investments (Figure 1). Respondents expect this trend to continue over the next three years, with future drivers of “analytics” investments including support for novel payment models, pursuit of annual revenue growth, fostering innovation, and enhancing consumer experience.
The focus of “analytics” investments reflects the priorities of value-based care (VBC), with clinical and population health emerging as the largest areas of “analytics” investment (Figure 2). Although financial management “analytics” was once a priority in healthcare systems, respondents indicated that population health and clinical “analytics” will take its place in the coming year.
Function: The current “Analytics” function remains limited, yet advanced analytics are an urgent priority for achieving Value-Based Care (VBC).
Healthcare organizations are frequently asked about the maturity of their current “analytics” capabilities and the key priorities for future development in this area. Generally, the scope of “analytics” ranges from business intelligence (e.g., reporting, dashboards, and ad-hoc queries) and relational data warehousing functionalities to predictive and advanced analytics capabilities (e.g., data mining, self-service analytics, statistical analysis, predictive modeling, natural language processing, machine learning, big data, and cognitive computing).
Many surveyed healthcare organizations already possess reporting capabilities for multiple business functions. Popular features that healthcare organizations plan to invest more heavily in building over the next three years include business intelligence and advanced analytics (Figure 3). Academic medical centers and health systems with annual revenues exceeding $2 billion believe they already have advanced analytics capabilities. However, survey results indicate that academic medical centers show weaknesses in predictive analytics and market intelligence capabilities (data not shown).
Respondents plan to prioritize the addition of advanced analytics capabilities in clinical care, population health, and financial management over the next three years (Figure 4), indicating that value-based care (VBC) is a strategic priority for their business development.
Costs: For many healthcare organizations, determining the total cost of "analytics" is quite difficult; there are signs that "analytics" costs are rising.
Fifteen of the 50 surveyed institutions stated that they do not track their overall spending on “analytics,” indicating potential fragmentation and lack of coordination in current “analytics” capabilities (Figure 5). Seven healthcare organizations claimed that their “analytics” budget accounts for 0% of their current operational budget; this may be partly due to the broad definition of “analytics.” The survey also revealed signs of growing expenditure on “analytics”: five of the surveyed healthcare organizations reported that their “analytics” budget currently represents 1%-5% of their operational budget, with projections to reach 6%-10% within the next three years.
Compared with non-academic medical centers and health systems with annual revenues of $2 billion or less, academic medical centers and health systems with annual revenues exceeding $2 billion are more likely to have higher “analytics” budgets (data not shown).
It is important that the level of investment in “analytics” aligns with the likelihood of achieving success. Healthcare organizations that allocate 1%–5% of their operational budgets to “analytics” have achieved success in certain functional areas (Figure 6). This may be partly attributable to the maturity of their analytics initiatives and the amount invested.
For example, over the past few years, many healthcare organizations have invested in electronic medical records (EMR), annual revenue cycle management (RCM), and enterprise resource planning (ERP) solutions, often accompanied by investments in business intelligence and basic analytics capabilities. Compared to other functional areas, these healthcare organizations reported greater success in financial management, clinical management, and enterprise performance management. A small number of respondents also noted improvements in population health management, though this is more likely attributable to their earlier adoption of value-based care (VBC).
Success: Healthcare systems with more mature analytical capabilities are more likely to achieve business success.
Healthcare systems that leverage “analytics” for more mature applications (such as advanced analytics and forecasting) are more likely to succeed (Figure 7). The more successful enterprises become through “analytics,” the more likely they are to invest in other advanced analytics solutions, thereby deriving further benefits and perpetuating this virtuous cycle.
Data Management Model: The “analytical” management model is fragmented, inconsistent, and variable.
Robust management models can help healthcare systems focus on priorities and strive to derive value from analytical insights. Although the surveyed healthcare organizations exhibited certain elements of strong data management strategies, many still lacked a centralized management model (Figure 8). Only 20 healthcare organizations had a clear and comprehensive strategic deployment for analytics across various business functions, although healthcare organizations with annual revenues exceeding $2 billion were more likely to claim having a comprehensive strategy (data not shown).
Of the 50 respondents, 21 stated that they lacked a formal enterprise-level data management process; only six had a Chief Analytics Officer (data not shown). However, three-quarters of healthcare organizations have established a department dedicated to providing enterprise-wide analytics (data not shown).
Nearly half of the surveyed organizations lack centralized oversight of “analytics” tasks and capabilities (Figure 9). The management of “analytics” architecture is often driven by IT organizations, while other functional “analytics” applications, such as workforce utilization, budgeting, and planning, are more likely to be driven by functional business departments.
Barriers to Adopting “Analytics”: Cultural Differences and Fragmented Ownership Are the Greatest Challenges
Culture, decentralized ownership, and access to technical resources are the biggest challenges facing the adoption of “analytics” in healthcare systems (Figure 10). Data access is the least significant barrier, but nearly half of respondents indicated that data quality is also an obstacle. For smaller healthcare organizations (with annual revenues between $50 million and $100 million), acquiring technical resources and securing corporate funding for “analytics” remain significant barriers to their adoption of “analytics” (data not shown).
Insights
Survey results indicate a positive correlation between the maturity of healthcare systems’ “analytics” initiatives and their success. Conversely, organizations lacking an enterprise-level “analytics” strategy, structured data management practices, and coordinated “analytics” investments are less likely to achieve comparable levels of success through analytics.
Healthcare organizations will require more sophisticated tools and capabilities to integrate, analyze, and leverage their data, thereby achieving Value-Based Care (VBC). The following recommendations can help healthcare organizations adopt “analytics”:
• Cultivate leaders committed to understanding and leveraging “analytics” to enhance corporate performance.
• Implement a structured data management model and an enterprise-wide “analytics” strategy.
• Manage “Analytics” capabilities and investments to drive innovation in functional business units and projects, enhancing their tangible value.
• Emphasize data and technical standards to promote interoperability and enable more effective utilization of “analytics” resources.
• Recognize the cultural dimensions of leveraging “analytics” to accelerate performance drivers.
Note: 1. The term “healthcare system” in this text refers to healthcare systems, academic medical centers (AMCs), and large hospitals.
2. “Analytics” refers to the systematic use of technologies, methods, and data to gain insights, enabling decision-making, planning, management, operations, measurement, and learning based on actual conditions.
Compiled by Chen Xin
Editor: Mo Renying
Download the Original English Version