Three key areas where healthcare IT leaders can deploy AI to improve patient outcomes

Healthcare providers face formidable challenges in applying AI capabilities to patient care. Efforts to do so are testing the current regulatory framework. For example, the U.S. Food and Drug Administration (FDA) is actively involved with stakeholders to create relevant policies that facilitate AI innovation while protecting patient safety.

Healthcare IT leaders can begin using AI tools and capabilities to improve patient outcomes now. Partnering with an IT solutions provider like TCS – with proven healthcare, AI and cloud expertise – can speed up testing, assessing, and deploying AI. TCS’ close collaboration with Microsoft enables TCS to leverage the AI tools and capabilities of Microsoft for healthcare-related AI projects.

TCS outlines three opportunities for IT leaders to improve patient outcomes with AI. These include: 

• Fully utilizing patient-related data
• Personalizing the patient experience
• Easing administrative burdens on healthcare professionals

To realize these opportunities, IT leaders can take the following actions:

1 – Consolidate patient data to fully leverage AI

With tools such as Microsoft Azure and Microsoft Fabric, healthcare IT leaders can establish a consolidated, standardized source of patient data. This is possible using Gen AI in data engineering tasks, including ingestion, engineering, analytics, and data science.

Sensitive healthcare data is fully encrypted, and TCS and Microsoft collaborate to ensure it meets all federal and state compliance requirements. Establishing a consolidated, trustworthy cloud data foundation is the critical basis for successful future AI applications.

IT leaders can take these first steps to get started:

• Identify the critical legacy data sources needed for each AI use case
• Replicate isolated legacy data sources into a unified, cloud-based data lake.
• Ensure a unified data governance layer for data quality, profiling, and compliance.

2 – Personalize the patient experience

Patients want to be seen and known. Healthcare providers already have data, from numerous interactions (physical and digital) with their patients. AI capabilities can leverage an individual’s data with existing patient processes to create a more complete picture of each patient.

AI alone can’t solve personalization issues. But once an organization successfully leverages the cloud to consolidate disparate data sources, AI can make an impact.

Non-expert as well as expert users can leverage Microsoft Copilot to sift through patient data and “translate” it into terms understandable to patients. Examples include quickly converting and analyzing data from various medical reports; summarizing data (in natural language) to track clinical trials; and automatically generating data analyses and reports.

IT leaders should consider these first steps to get started:

• Identify and prioritize use cases where AI capabilities can deliver modest but real workflow and productivity improvements for the patient’s journey. Examples include improving the patient portal, adding virtual assistants, scheduling appointments, managing healthcare escalations, and even triaging patients.

• Leverage retrieval augmented generation (RAG) architecture (used in Microsoft Azure) in GenAI solutions to enable comprehensive, accurate, personalized, context-aware response to patient requests or prompts, based on your enterprise’s data or knowledge sources.

3 – Free up healthcare professionals to focus on patients

The heavy administrative burden on healthcare practitioners and staff is well-known. Microsoft AI capabilities can ease this burden when astutely applied. Microsoft copilots are already automating routine workflows such as creating notes of patient encounters, scheduling, summarizing patient charts; and more advanced uses in diagnostics, such as image analysis.

Shifting these tasks to AI frees up practitioners to focus on higher-value, patient-oriented tasks and direct patient interactions.

IT leaders should consider the following when starting the AI journey:

• Create a matrix of value vs. risk for each workflow use case, assessing both factors realistically.

• TCS has assessed the following workflows in terms of value and risk:
  • Patient portal improvements                high value; low risk
  • Generating physician notes                   high value; low risk
  • Patient scheduling                                    low value; low risk

• Other options can have higher visible impact on improving employee effectiveness but carry higher risk:
  • Patient chart summarization                high value; high risk
  • Automating patient documentation  high value; medium risk

The bottom line

Healthcare IT leaders can begin using AI tools and capabilities now to quickly improve patient outcomes, often indirectly but measurably.  Choosing the right IT solutions partner can speed specific AI implementations and create a foundation to support additional AI applications in the future.

To learn more, visit Patient-centered Care: Health and Wellness Redefined.