A patient is more than their symptoms and more than their medical records. Keeping that larger, human perspective in mind can deliver better outcomes.
Some of the most promising opportunities in healthcare today come from using non-clinical data to improve patient engagement and outcomes. Looking beyond electronic medical records (EMRs) means we gain visibility into not just patients’ conditions, but their preferences, needs, challenges, and resources. With this visibility, healthcare companies can implement best practices, using artificial intelligence and machine learning to create empathy to enhance the patient experience and improve patient outcomes.
The World Health Organization (WHO) defined health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” more than 70 years ago. However, healthcare providers still tend to structure treatment based purely on medical knowledge alone.
Today we can take a more holistic view because we have many more sources of clinical and non-clinical data available. By augmenting EMRs with real-world data from patient-recorded outcomes, patient preferences and challenges, community and economic data, remote sensors and wearables enabled for the Internet of Things (IoT), we can create a more complete view of the patient. This allows us to better engage, manage and support patients. Most healthcare organizations can benefit from implementing systems that focus on three areas: identifying gaps in care, addressing social determinants of health, and matching needs with programs.
Identifying gaps in care
Gaps in care are the differences between the recommended care plan of a patient and what happens in the real world. Patients miss appointments or screenings. Referrals to specialists or follow-up visits aren’t pursued. Rescheduling notices aren’t processed correctly. These all impact patient outcomes.
The challenge with gaps in care is identifying when they occur. How do we quickly recognize that the patient is no longer following the optimum path and then, through communication and engagement, bring that patient back to that recommended care plan? Using all the data at our disposal can be the key. We can use advanced analytics and machine learning to detect gaps much more quickly. Advanced patient communication capabilities allow us to engage quickly with the patient to address any care delivery issues preventing improved treatment.
Without a strategy to collect and include data beyond EMRs, it is difficult to identify any potential gaps, which can result in avoidable suffering, hospital admissions and costs.
Addressing social determinants of health
Social determinants of health are environmental and social factors that affect a wide range of health outcomes—these include income, education, access to care, access to healthy food and environmental hazards. Small investments in addressing social determinants at the individual level can mean big improvements in these areas.
Improving access and quality of care for hard-to-reach patient groups is a major priority of value-based care initiatives. Relying on patient records alone addresses very few of these issues. Healthcare organizations need to analyze a range of non-clinical data to make informed decisions about the structure and execution of care services.
Matching needs and programs
To help address social determinants of health, healthcare organizations are integrating patient social support navigators into primary care teams. To be fully effective, support navigators require data and analytics to match patients with programs. AI and machine learning provide significant advances in technology, making it easier to quickly identify and apply for available services that will contribute to a healthier patient.
Optimizing engagement and communication
These priorities rely on creating engaging, ongoing interactions with patients. Ultimately, high-quality healthcare requires patient engagement if we are to maximize the quality of life for patients. Patient engagement has been a C-level issue in healthcare for some time, but the industry is now uniquely positioned to bring together clinical and non-clinical data to drive engagement and build better communication with patients.
People have strong personal preferences in how they want to interact—voice, text, email, etc.—and what level and frequency of interaction works for them. As in other parts of their lives, patients want communication to be personalized, contextual, and timely. This increasing focus on creating engaging and valuable interactions with patients can improve health outcomes.
Creating high levels of engagement requires us to build a sense of empathy with the patient, and this requires artificial intelligence and machine learning. Developing engaging, personalized and data-driven communications need an integrated view of the patient that includes all available data. The ability to capture, organize, analyze and respond are key capabilities for the healthcare organization of the future.
Practical Implications & Cloud Modernization Strategy
Migrating core commercial applications and legacy workloads into modern hyperscale cloud architectures requires a phased modernization strategy that balances execution speed with disciplined financial governance across the entire corporate portfolio. Enterprise architects must perform rigorous dependency mapping to determine optimal workload placement—evaluating managed containerized runtimes, cloud-native serverless functions, and dedicated software-as-a-service platforms against strict latency, security, and sovereignty requirements. Implementing automated infrastructure-as-code pipelines, standardized policy definitions, and centralized identity enforcement guarantees enterprise landing zones maintain robust architectural governance, predictable system performance, and strict regulatory adherence across dynamic production environments.
Long-term modernization success fundamentally hinges on mature cloud financial operations (FinOps) and continuous platform performance optimization across multidisciplinary engineering and finance teams. Establishing live telemetry dashboards, automated workload elasticity policies, and rigorous capacity planning mechanisms empowers infrastructure managers to eliminate redundant resource consumption while scaling compute dynamically to meet volatile commercial demand. Organizations that deliberately align cloud modernization milestones with measurable strategic business outcomes consistently accelerate digital release velocity, elevate operational reliability, and unlock superior stakeholder value from modern enterprise technology investments. This structured alignment provides the operational stability required for enduring digital maturity.
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