Build a Holistic Data Strategy for Your Healthcare Organization

Develop a data strategy to become a data-driven organization and maximize the value of data.

Analyst perspective

Maximize the value of your data to become a data-driven organization.

The rapid increase in healthcare data volume presents a significant challenge for organizations aiming to leverage this data for strategic insights and informed decision-making. The lack of integrated systems and data silos, coupled with regulatory compliance requirements, often hinder effective data management. Additionally, competing priorities, cybersecurity threats, and limited data literacy further complicate the ability to harness data’s full potential. These obstacles highlight the need for a cohesive strategy to optimize data assets, particularly in the context of AI applications and health information exchanges.

This research aims to address these challenges with a comprehensive, step-by-step process that includes identifying business drivers and value opportunities, updating processes and architectures, defining metrics to measure the impact of data strategies, aligning organizational structure and governance frameworks, mapping technical capabilities to process and analytics needs, and formulating a communication plan to convey the strategy to stakeholders. By following this structured approach, healthcare organizations can better position themselves to unlock the value in their data and drive more effective decision-making.

Sharon Auma-Ebanyat Research Director, Healthcare Industry Info-Tech Research Group

Executive summary

Your Challenge

Keeping up with digitization in healthcare will grow more complicated as the volume of data in healthcare rapidly increases.

Your organization lacks a cohesive strategy to optimize data assets, leaving its data ecosystem fragmented. This deficiency has far reaching implications, particularly in the context of AI applications and effective health information exchanges.

Your organization needs to develop a clear data roadmap to enable the utilization of data for effective decision-making.

Common Obstacles

Your organization lacks strong data governance structures and resources that holistically maintain data quality and integration.

Competing priorities hinder investment in data analytics platforms.

Compliance with new regulations and cybersecurity threats make it challenging to leverage data potential while protecting data.

There is limited data literacy and staff resistance toward a data-driven culture in healthcare organizations at an individual, departmental, organizational, and community level.

Info-Tech’s Approach

Formulate a data strategy that stitches the pieces together to better position your organization to unlock the value in its data:

1. Identify business drivers and value opportunities.
2. Map business outcomes to processes, analytics, and data.
3. Define metrics to measure the impact of data strategy on business outcomes.
4. Map technical capabilities to process and analytics.
5. Establish an organizational structure & governance framework.
6. Communicate the data strategy.

Info-Tech Insight

Healthcare organizations need to develop a unified data strategy that addresses integration, compliance, and data literacy challenges. This strategy should align with business objectives and organizational drivers, enabling organizations to become data-driven by fully leveraging healthcare data for strategic insights and informed decision-making.

Healthcare is struggling with the growth and complexity of data

The exponential growth, quality maintenance, security, and interoperability of healthcare data present significant challenges due to the complexity and scale of managing diverse and sensitive information.

1. Storage challenges due to exponential data growth

   This growth is driven by the digitization of healthcare data and the adoption of electronic health records, connected devices, and IoT/IoMT. Finding cost-effective and scalable data storage solutions is a challenge.

2. Maintaining data quality and integration

   This is an ongoing challenge with inconsistencies capturing clean, standardized, complete, accurate data from multiple systems, hospitals, clinics, and laboratories. There is also an increase in data velocity and variety by way of the Internet of Things, medical devices, genomic testing, machine learning (ML), and NLP.

3. Keeping data secure

   Healthcare organizations are concerned about managing the risk of cyberattacks, which not only have financial implications but patient safety and privacy implications as well.

4. Developing data interoperability

   Data interoperability is still a patchwork system for public health management and value-based care, which has expanded reach to support population health and address social determinants of health and patient-generated data.

5. Compliance with new regulations

   Healthcare providers need to balance the benefits of data potential with regulatory protections. Personally identifiable information (PII) data can be removed when sharing results with pharmaceutical and medical research companies.

6. Data bias challenges that lead to care inequities

   Medicine has struggled to include diverse populations in its research despite knowing they have different risk factors for disease manifestations.

Healthcare organizations are lagging in data utilization and investment

Data utilization is lagging in healthcare organizations.

57% Only 57% of healthcare organizations’ data is being used for intelligent business decisions without a clear strategy for data analytics and AI. (Source: Arcadia, 2023)

Investments in data are lagging in healthcare organizations.

71% Competing priorities are a barrier to investment in data analysis platforms for 71% of healthcare organizations. (Source: Arcadia, 2023)

“Data must be high quality, accurate, and free flowing for health systems to trust and utilize it to guide decision-making. It’s equally critical that analytics synthesize data into useful information and actions to deliver quality care that improves patient outcomes.” (Kate Behan, MD, FACP, Chief Medical Officer, Arcadia)

Info-Tech Insight

Effective data utilization in healthcare is hindered by both a lack of strategic implementation and barriers to investment, yet it remains critical for informed decision-making and enhanced patient care.

Few healthcare organizations make mature, data-driven decisions

Healthcare has a long way to go to reach maturity.

Mature (Leaders): The organization can access, integrate, and analyze data from multiple diverse information sources that can support quick decision-making.

Maturing (Followers): Most data is digital, but there are workflow gaps and slow diagnosis and decision-making processes.

Immature (Laggard): Some electronic systems and technology exist for data management and collection, but it is challenging to identify and compile data quickly for decision-making.

(Source: Harvard Business Review, 2023)

“You can’t run a profitable health care organization without leveraging data in novel ways. You can’t see patients you need to see without leveraging the data of the past to find care journeys that are optimal for patients in the future. And you can’t reduce burnout in your clinicians unless you are looking at the data to determine which clinician needs to see which patients,” (John Halamka, MD, President, Mayo Clinic Platform)

Info-Tech Insight

Healthcare organizations that fail to embrace data-driven decision-making risk falling behind in delivering quality care, facing increased operational inefficiencies, and missing out on critical innovations that could improve patient outcomes.

Healthcare faces challenges with becoming data-driven

Incompatibility, funding, and collaboration are inhibiting healthcare organizations from becoming more data-driven.

Evaluate your data management maturity

Info-Tech’s IT Maturity Ladder denotes the different levels of maturity for an IT department and its functions. What is the current state of your data management capability?

Info-Tech Insight

You are best positioned to successfully execute a data strategy if you are currently at or above the Trusted Operator level. If you find yourself still at the Unstable or Firefighter stage, your efforts are best spent on ensuring you can fulfill your day-to-day data demands. Improving this capability will help build a strong data management foundation.

Healthcare organizations have low data and BI maturity

Required foundational IT capabilities

Level of maturity

Many IT departments don’t own responsibility for all the data and technologies across the business, causing a conundrum for many CIOs. AI models put much more emphasis on the need for clean data to deliver real-time, data-driven decisions. IT must usher in a new era of reliable, clean, enterprise-wide data and model services.

Benefits

• A solid plan for how data and models will be used by the business can enhance your decision-making.
• Clean data enables both predictive and prescriptive insights that can reduce operating costs.
• By cleaning and integrating data with AI models there is less need for expensive labor.
• Reduce costs and increase efficiency.

Challenges

• Many organizations have vast amounts of data polluted across structured, semistructured, and unstructured sources and struggle to keep it clean.
• Data cleansing is already difficult – now add the complexity of developing a process for producing, managing, scaling, monitoring, and improving analytic models for AI services.

Complete the IT Management & Governance Diagnostic to assess your core IT processes for improvement.

Download the IT Management and Governance Diagnostic

CIOs report low satisfaction rates for their data quality, analytic capabilities, and IT training

1. Healthcare CIOs are unsatisfied with their analytic capabilities and recognize the need for data improvement.
2. Three key areas pertinent to developing a data strategy are:
   • Data quality
   • Analytic capabilities
   • Quality of IT training for IT support services

(Source: Info-Tech’s CIO Business Vision Diagnostic 2021-2023, N=41 healthcare organizations)

Download the CIO Business Vision Diagnostic

Investment in healthcare data is growing

Four factors driving investment growth

1. Advanced technological infrastructure: The growing healthcare IT infrastructure supports superior data storage, processing, and analysis capabilities.
2. Increasing demand for personalized medicine: The increasing volume of electronic health records and the need for real-time decision-making are boosting the market for big data in healthcare, enhancing personalized medicine by reducing healthcare costs and improving patient outcomes.
3. Value-based care focus: North America’s emphasis on value-based medicine drives demand for data-driven insights to improve care quality and efficiency.
4. Increased demand for population health analytics: The demand for population health analytics is driving market growth by improving care management, early sickness prediction, and hospitalization processes through the integration of clinical and claims data for cost-effective patient care.

Global Healthcare Big Data Market Size From 2023 to 2032 (in billions $USD at CAGR 18.48%)

(Source: Verified Market Research, 2024; Market Research Future, 2021)

Healthcare organizations need to focus on maximizing the value of their data

While healthcare leaders struggle to fund their data initiatives, organizations that invest in their data and have high data maturity levels report exponential ROI over time.

Healthcare organizations are gaining focus on monetizing data.

(Source: Hakkoda, 2023)

Healthcare organizations that invest in data get significant returns.

124% — Data investments lead to an average 124% ROI for healthcare organizations. (Source: Hakkoda, 2023)

“I believe the main challenges of data-driven innovation in healthcare lie not in a lack of appreciation for or use of data but in a misunderstanding of its comprehensive power and holistic applicability. From diagnostic tests to coding profiles, healthcare data is often viewed in a vacuum by the stakeholders it directly applies to, rather than holistically.” (Kathryn Watson, Head of Customer Success, Coalesce)

Info-Tech Insight

Many healthcare organizations plan to monetize data in the future, but misconceptions about its comprehensive power and holistic application remain significant barriers.

Developing your data strategy provides long-term value

1. Improved patient outcomes at reduced costs
2. Prevention of data-breaches
3. Compliance with regulatory requirements
4. Effective decision-making
5. Streamlined operations and higher patient satisfaction
6. Increased ROI and revenue
7. Effective AI adoption and innovation

“Data must be high quality, accurate, and free flowing for health systems to trust and utilize it to guide decision-making. It’s equally critical that analytics synthesize data into useful information and actions to deliver quality care that improves patient outcomes.” (Kate Behan, MD, FACP, Chief Medical Officer, Arcadia.)

Build a Holistic Data Strategy

Elements of a successful data strategy

1. Accuracy
2. Accessibility
3. Scalability
4. Automation
5. Security and Privacy

Download Build a Robust and Comprehensive Data Strategy

Your business strategy is the north star of your data strategy

Starting a data strategy can be overwhelming for many healthcare organizations. Start by aligning your data strategy priorities with your business goals.

Your business strategy informs your data strategy and vice versa, which makes it easier to stay aligned and determine priorities for your data strategy.

Business goals to consider

What are the driving forces behind changes and decisions within the business?

1. Deliver world class experience for patients and their families
2. Expand equitable access to high-quality care
3. Achieve operational excellence and efficiency
4. Develop a clinical team of today and the future

Info-Tech Insight

As a data strategy leader, start by engaging with executive leaders of different business areas to capture their data priorities. Identify any data gaps that will drive business decision-making aligned with the organization’s business strategy and goals.

A structured approach to your data strategy

Good business goals for healthcare data analytics are essential for improving patient outcomes, optimizing operations, and driving innovation.

Key drivers to consider when building your data strategy:

1. Enhancing patient care: Use analytics to provide personalized care plans, predict patient risks, and improve treatment outcomes.
2. Operational efficiency: Apply data analytics to streamline hospital operations, reduce wait times, and improve patient flow and throughput.
3. Financial performance: Use analytics to manage costs, optimize resource allocation, and improve billing accuracy.
4. Quality improvement: Focus on outcome measures like mortality rates, readmission rates, and variable costs per case to drive quality improvements.
5. Strategic decision-making: Leverage predictive analytics for informed decision-making and strategic planning.
6. Regulatory compliance: Ensure that analytics help you meet healthcare regulations and standards.
7. Technology integration: Integrate advanced analytics tools with existing healthcare IT systems for seamless data analysis.
8. Innovation and research: Harness the power of data to fuel research and development, leading to new treatments and technologies such as AI and ML adoption in analytics.

Start with core performance data metrics in these areas.

• Finance/Revenue Cycle Management (RCM)
• Clinical Quality and Research
• Patient Experience
• Accreditation (JCO, MIPS, MGMA)
• Risk Management, Privacy, and Security
• Human Resources
• Marketing/Awards

Align with executive leaders in various business areas to help you prioritize business goals and drivers that are important for developing the organization’s data strategy. These should be traced back to the organization’s current strategic goals/plan.

Determine value opportunities for your data pertaining to revenue, cost, and risk.

Many organizations emphasize the importance of managing and leveraging data as an asset to enhance business outcomes. This often involves high-level discussions about:

• Accelerating digital transformations.
• Making faster and more strategic decisions.
• Improving business agility and becoming more customer-centric.
• Focusing resources on value creation.

While it’s widely accepted that better data can drive these objectives, securing resources and support for data governance and management requires targeted conversations with business leaders. These discussions should align with the business leaders’ specific goals and initiatives to effectively execute a data strategy. Below is an example of data value opportunities to discuss.

Data Value Opportunities Illustration

Outline the vision and mission of your organization’s data strategy.

The organization’s data vision and mission

Data vision statement

• Your vision statement ensures that your organization is aligned to a common goal. Your data vision statement should align with your organization’s overall vision statement, which should provide answers as to why you care about data and why data organization and processes exist.
• Your data vision is the north star for your data strategy. Failure to establish one can lead to costly initiatives and project rework.

Characteristics of a strong data vision

• Describes a desired future
• Focuses on ends, not means
• Communicates promise
• Concise
• Compelling
• Achievable
• Inspirational
• Memorable

Data mission statement

• Your data mission statement declares what you are going to accomplish and how you are going to accomplish it with a data strategy. It supports the data vision statement.
• Your mission statement should be action oriented:
  • The what: What are you going to accomplish with your data?
  • The how: What does process of accomplishing it look like?

Characteristics of a strong data mission

• Articulates data strategy, purpose, and reason for existence
• Describes what the data strategy function does to achieve its vision
• Defines the customers of the data strategy
• Compelling
• Easy to grasp
• Sharply focused
• Inspirational
• Concise

1.1.1 Craft your vision and mission statements and guiding principles

1-3 hours

Input: Business context as derived from stakeholder interview session and input

Output: Vision and mission statements and guiding principles for the data strategy, Data Governance Implementation Plan Template

Materials: Whiteboard/flip charts

Participants: CDO/CIO, Director of data analytics, Data architect, Enterprise architect, Business analyst, Senior leaders and business stakeholders

1. Gather key stakeholders in your organization to create unified vision and mission statements and guiding principles for your organization's data strategy.
2. Have each participant create a statement of purpose (1-5 lines) describing the future data management practice. Have them consider the following:

   Vision:

   • What does an organization with an effective, high-value data strategy look like?
   • How will our organization benefit and grow from an improved data strategy?
   • Reflect on current-state data collection: What are our customers saying, feeling, and doing?

   Mission:

   • Why does this program exist?
   • What problems are we trying to solve?
   • Who will benefit from this program?
   • How will we reach our target?

   Guiding principles:

   • What ethical considerations should we keep in mind?
   • How do we ensure data privacy and security?
   • What standards and practices will we follow to maintain data quality?
3. Ask each participant to present their vision and mission statements and guiding principles. Discuss common themes, then develop concise vision and mission statements and guiding principles that incorporate the group’s ideas.
4. Consolidate the findings and document the results.

Download the Data Governance Implementation Plan Template