Taking Advantage of Data and AI for Better Healthcare Outcomes
Data platforms can help healthcare organizations achieve effective governance and set them up for AI success.
- AI HEALTHCARE CHALLENGES
- AI SOLUTIONS AND SERVICES
- AI USE CASES AND OUTCOMES
Challenges to AI adoption include legacy technologies and cultural resistance to AI-enabled workflows. Suitable IT infrastructure is the baseline requirement from a technology perspective, but even a modernized environment may be unprepared for AI if organizations lack foundational governance and an overarching strategy to guide decision-making.
LACK OF AI STRATEGY: A common misstep is treating AI as a solution in search of a problem. A better approach is to identify the problem and then determine whether AI provides an effective solution. An AI strategy helps leaders make these determinations while ensuring that AI investments align with IT and business objectives.
INSUFFICIENT GOVERNANCE: IT teams and line-of-business personnel should contribute to data governance (the policies and processes that guide an organization’s approach to data privacy, security, stewardship and other areas). Governance frameworks should also address risk, decision-making, AI product selection and ROI metrics. Without proper governance, poor data quality will impair AI outcomes.
POOR DATA QUALITY: Data quality encompasses not only accuracy, integrity and completeness, but also timeliness, consistency, validity and uniqueness. It is a cornerstone of AI initiatives, data-driven decision-making and operational efficiency. Having an effective approach to improving and maintaining data quality ensures that organizations can trust their data to generate reliable AI outputs.
INEFFECTIVE CYBERSECURITY: To use AI securely, organizations must know how tools are used, who has access, what the data inputs and outputs are and how data is secured. Cybersecurity professionals say their top concern about generative AI is that a large language model could potentially expose sensitive data, a significant compliance risk.
NEED FOR AI SKILLS: AI skill building is essential, but an AWS survey found that 78% of U.S. healthcare employers are unsure of how to implement an AI training program. Expert partners can help organizations develop appropriate role-based training; for instance, executives require fluency in data literacy, while administrative teams may need training in prompt engineering.
Click Below to Continue Reading
The Future of AI in Healthcare
Between AI systems rapidly improving their clinical knowledge and medical device makers quickening the pace of innovation, AI in healthcare is poised for expansion. In some of the most promising applications, researchers are exploring the use of AI to forecast viral evolution and inform pandemic preparedness, diagnose Alzheimer’s and other neurodegenerative diseases more quickly and understand genetic mutations.
Medical Imaging: New research suggests that combining AI systems and traditional clinical methods to diagnose medical imaging may deliver better results than clinicians or AI alone.
Machine Vision: Two-way cameras in patient rooms and other spaces set the stage for expanded applications of machine vision, such as virtual nursing and data capture in operating rooms.
Care Administration: An AI model that incorporates EHR data sets could significantly alleviate clinicians’ administrative workloads. In 2024, researchers moved closer to this capability with a new, EHR-focused benchmark.
Synthetic Data: Created by generative AI systems, synthetic data mimics real-world data to allow for research, development and machine learning while protecting actual patient information.
AI-focused solutions and services empower healthcare organizations to establish secure, reliable and scalable data ecosystems.
Solutions
Data platforms: Modern platforms offer centralized data management, integration and processing crucial to AI. Designed for large-volume data, these tools may integrate with EHRs and offer key capabilities such as advanced analytics, machine learning and AI model operationalization.
Data governance solutions: These solutions ensure data is managed, secured, accessed and used appropriately. They help organizations evaluate data quality, enforce role-based permissions and other protocols, and provide transparency into how data is being used.
Infrastructure: Many organizations must upgrade compute power and storage for AI. High-performance computing clusters speed AI model development, while graphics and Tensor Processing Units enable parallel processing. Organizations may also adapt cloud strategies; for instance, leveraging on-premises data centers for low-latency, real-time insights.
Off-the-Shelf AI Tools: Solutions such as machine vision and ambient listening use AI to significantly reduce the burdens on clinicians. For example, these tools can free up healthcare workers from tasks such as patient monitoring by analyzing real-time data from cameras and microphones set up in patient rooms.
Click Below to Continue Reading
Services
CDW helps healthcare organizations address their unique challenges so they can pursue AI objectives more strategically.
DATA ECOSYSTEM EVALUATION: Organizations that build a solid AI foundation and define objectives clearly are more likely to achieve desired outcomes while minimizing risks. CDW’s AI ReadiData Ecosystem Evaluation assesses readiness for AI, machine learning and analytics initiatives so that organizations can align their data ecosystems accordingly. By conducting a gap analysis, CDW identifies opportunities to improve ecosystem capabilities in data ingestion, storage, processing, analysis and sharing. Additional benefits include mitigating risks related to cybersecurity, compliance and technology obsolescence, along with optimizing resource use and enabling informed decision-making. As a result, organizations receive expert-driven insights to help them create a robust, efficient data ecosystem that drives business value, agility and innovation.
DATA ECOSYSTEM DESIGN: CDW’s ReadiData Modern Data Ecosystem Design Workshop helps organizations architect a data ecosystem that aligns with their business goals. Through a robust discovery process, the workshop clarifies objectives and develops a roadmap to guide ecosystem deployment and establish criteria for measuring the success of AI initiatives. The service includes architecture design and strategy, delivering a customized, scalable blueprint to support evolving data strategies and advanced analytics. It is ideal for healthcare organizations seeking expert insights and best practices to optimize data-driven decision-making, align data platforms with specific needs and establish a secure, scalable infrastructure for long-term growth.
DATA QUALITY DESIGN: Data quality is essential for successful AI initiatives, and many organizations benefit from expert guidance. CDW’s ReadiData Data Quality Design Workshop offers a practical approach to improving data quality. The workshop equips organizations with the tools and knowledge to assess their data quality management practices and develop a strategy for enhancement. High-quality data enables better decision-making, reduces the chance of costly errors and redundancies, and improves operational efficiency. For organizations aiming to personalize the patient experience, accurate data provides key insights and guides strategic investments. Beyond AI, data quality offers a competitive edge by helping organizations drive growth and identify business opportunities. This workshop helps organizations make immediate improvements and establish sustainable practices for maintaining high-quality data.
DATA AND AI GOVERNANCE DESIGN: Effective data governance provides clear guidance and guardrails for AI initiatives, ensuring that organizational objectives drive the technology rather than the other way around. With the right foundation, organizations can trust their data and evolve their AI strategies confidently. CDW’s ReadiData Data Governance Design Workshop delivers a holistic plan to align data management with best practices. A current-state assessment informs the development of a customized roadmap to implement a robust data governance framework. Participants leave with an actionable plan and tailored product recommendations to address gaps and achieve desired outcomes. This process often helps leaders clarify goals, align on terms and enhance collaboration, simplifying the path to successful AI initiatives.
AI is empowering healthcare organizations to address their most pressing challenges, from clinician burnout to the financial complexity of modern care.
Patient Care and Engagement: Healthcare leaders have high hopes for generative AI, with 62% predicting it will improve the patient experience. For instance, generative AI enables providers to leverage structured and unstructured data to support patients via chatbots (secured through appropriate firewalls). For clinical care, AI-enabled devices help clinicians automate the tracking and evaluation of vital signs, while care teams in virtual command centers use AI-powered analytics to flag patients’ health changes. Computer vision — AI embedded in cameras and optical sensors — is enabling numerous use cases, from speeding up disease detection to facilitating real-time surgical guidance. Increasingly, AI will allow for the delivery of personalized care, with data-driven insights improving clinical decision-making and providing tailored supports to help patients manage their health more effectively.
Predictive Analytics: Leveraging data to understand and optimize today’s operations is powerful; using data to forecast and guide the future is transformative. Clinical applications include using predictive analytics to identify patients at risk for adverse outcomes, analyze medical histories to determine the likelihood of specific diseases and increase patient safety by predicting medication side effects or surgical complications. Predictive analytics are also becoming essential for operational efficiency. For example, providers that can more accurately predict patient admissions can allocate staffing appropriately, control costs and reduce patient wait times. Predictive capabilities also allow for safer, faster development of new treatments; for instance, by effectively matching individual patients with clinical trials. Together, these advances lead to better care and more efficient operations.
Click Below to Continue Reading
Administrative Efficiency: Administrative work accounts for a sizable percentage of total healthcare costs, making AI a valuable tool for financial health. AI-powered automation reduces costly, time-consuming manual tasks and streamlines the work of medical coding, claims processing and revenue cycle management so that staff can focus on higher-value activities. For example, automating coding and claims processes improves compliance and reduces errors that can lead to claim denials. Administrators are also using AI to optimize staffing and inventory, forecast expenses, simplify patient scheduling, and facilitate access to organizational policies and best practices. Organizations are also accessing these capabilities through third-party partners and managed service providers that leverage AI in their own tools and platforms.
Clinical Documentation: AI-powered tools that use ambient listening and natural language processing to document patient conversations are helping providers reduce one of the biggest contributors to clinician burnout. One study found that 28% of medical groups are using this technology so far; some nonusers said they were waiting for their EHR platform to add the capability, or their organizations had not yet evaluated the use of these tools. Ambient listening can significantly reduce the amount of time clinicians spend updating EHRs and allow them to be more attentive and engaged with patients. Further, because physicians must verbalize their actions so that the listening tool can record them, the resulting documentation is often more comprehensive, leading to more effective patient handovers between clinical staff members.
Healthcare organizations are putting AI
to work to improve patient care,
ease clinical workflows and
increase operational efficiency.
Lee Pierce
Healthcare Strategist
Ben Sokolow
Healthcare Strategist