Healthcare professionals encounter valuable data every day, whether it's critical observations scribbled in clinician notes or structured lab results. The challenge is taking this data and turning it into actionable insights while maintaining the highest standards for data integrity.
Ensuring clean data is the first step.
Health data must be free from errors, bias, and stale information before being analyzed. Otherwise, analysis models will produce flawed insights, such as spurious correlations that suggest meaningful patterns that don't exist or inaccurate prediction data for early-warning systems.
This article aims to make sense of complex data management practices such as integration, aggregation, machine learning, visualization, centralization, interoperability, RBAC, and structured reporting.
Breaking Down Data Silos
Clean data starts with breaking down silos that keep essential data trapped and scattered across various systems.
For instance, a patient's electronic health record (EHR) rarely tells the complete story. Their profile could be missing critical data from pharmacy management systems, insurance claims databases, and wearable medical devices. This data must be aggregated into unified platforms to gain clear insight.
When breaking down these silos to understand a patient's health profile, you'll also uncover multiple unstructured data sources, like clinician notes, pathology reports, and X-Rays.
Suppose a patient is a participant in a clinical medication trial. Creating structured chart data from these sources is critical to understanding adverse side effects, timelines, and progress. A medical chart abstraction team then identifies, validates, and standardizes specific data for reporting.
Aggregating and integrating data can also bring more clarity to care teams trying to understand a rise in emergency room visits for a patient. After analyzing processed clinical and claims data, the team notices that the patient has missed some critical medical refills.
Advanced Analytics and Machine Learning
To understand underlying health trends and predictive patterns, data scientists use sophisticated machine learning (ML) models. These analytical models yield the type of precise data needed for proactive treatments and preventative care.
From a public health perspective, healthcare professionals can reach target populations earlier for preventive care campaigns. But a commitment to data integrity remains critical, as good public health initiatives rely on unbiased, relevant historical data to calculate accurate probability scores for disease risks.
Advanced analytics also improves administration efficiency by accurately forecasting patient admission rates. This allows healthcare organizations to optimize staff scheduling and clinical resource allocation.
Data Visualization
Data visualization allows analysts to present complex insights to a broad range of internal and external stakeholders, from clinical trial patients and IRBs to boards of directors and regulatory agencies. Dynamic dashboards are one such example, presenting a combination of visuals, including:
- Real-time KPI charts
- Heat maps
- Infographics
- Process flow charts
- Geographic Information Systems (GIS)
These visualizations allow key stakeholders to track regional health trends and key performance indicators for hospitals.
On a doctor-patient level, data dashboards allow clinicians to better visualize longitudinal health trajectories for more timely and targeted interventions. This is an example of the human interpretation of data.
While machine learning models have certainly advanced healthcare data analysis, human insight is still critical. Metrics alone cannot inform empathetic care strategies. Professional interpretation adds essential nuance and context to complex data insights.
Data Centralization
When you consider the sheer amount of data generated from one healthcare organization alone, the volume is simply staggering. That's why these organizations need scalable data management infrastructures, starting with clinical data warehouses. These centralized repositories store data from various systems, including:
- Electronic Medical Records (EMR)
- Radiology Information Systems (RIS)
- Laboratory Information Systems (LIS)
- Picture Archiving and Communication Systems (PACS)
This data is then standardized and structured for mass analysis. Importantly, data can be quickly retrieved for research, policy updates, or virus tracking, for example.
Modern healthcare networks are also integrating cloud-based data storage systems. This transition is allowing for more scalability beyond on-site servers. Cloud storage solutions can still process massive health datasets, including complex 3D MRIs and genomic sequencing data.
Cloud storage systems connect global healthcare networks so that data can be securely accessed from various medical sites. For example, a general physician and a specialized surgeon can access the same data and work collaboratively on a patient's care plan, regardless of geographical distance. This improves response times, an essential quality metric used by the CMS to assess quality ratings.
Interoperability
Interoperability is the seamless ability of different healthcare systems and technologies to exchange and process data without compatibility issues. This spares the need for manual data re-entry, which can pose the risk of human errors and poor data quality. Patients' EHRs can be easily sent to other medical centers that may have different computer operating systems.
Interoperability is also a healthcare industry standard, as exemplified by the Fast Healthcare Interoperability Resources (FHIR) standard.
For instance, FHIR is what allows mobile medical devices to securely share data with EHR systems. It accepts a wider range of formats, such as XML and JSON, unlike older data management systems. FHIR also translates all transmitted information into a universal format that can be read by any authorized transactional system.
Role-Based Access Control (RBAC)
Storing and processing large datasets requires a strict security framework that includes Role-Based Access Control (RBAC). A healthcare RBAC system operates on a need-to-know basis.
For example, while a triage nurse is given access to view a patient's allergy history, a medical billing specialist is only given access to view the diagnostic codes. Therefore, a strict RBAC system can still enable workflow without unauthorized data exposure.
Structured Reporting
Healthcare can move quickly, generating massive amounts of unstructured data. Some of the most valuable data is unstructured, such as:
- Clinician notes with timelines
- Audio dictations
- CT scans
- Nurse reports
- Discharge summaries
- Wearable device data
To speed up processing and ensure accuracy, analysts use Natural Language Processing (NLP) tools to extract and organize data for analysis. Outsourcing this process can relieve administrative burden while maintaining data integrity. For example, leveraging machine learning and outsourced services can speed up a critical cardiovascular study report to streamline a CVD clinical trial.
Clinical Decision Support
Structured data reports allow for more timely decision-making, since clinicians don't have to parse out critical information from a previous doctor's notes or match up a timeline from past patient feedback.
Clinical Decision Support (CDSS) systems built on structured data accurately match EHR data with standardized knowledge bases to support evidence-based care. CDSS systems are also designed to alert clinicians to medication interactions. Suppose a patient is taking a prescription for allergies; the CDSS will trigger a warning if a clinician looks up a blood pressure medication.
Structured data reduces the likelihood of a physician omitting a critical diagnostic detail during a patient evaluation. If the same physician needs to refer their patient to a specialist, a standardized, structured report will be much easier to navigate, as specific information will be validated and highlighted. This spares specialists from having to read through dense, narrative text.
Turn Data Into Actionable Insights
Clear data insights bring all stakeholders together, from patients who need clarity about health outcomes to regulatory agencies that need structured reporting for quality care assessments.
Start by solving the data silo problem, followed by the integration of ML-built analytics, data visualization with dynamic dashboards, cloud-based centralization, FHIR Interoperability standards, RBAC security, and structured reporting for clinical decision support.
These insights support better patient experiences, both in-person and digitally. Follow our blog to optimize digital experiences with the latest strategies in accessible, responsive designs.