Closing the Gap on the Top Challenges in Healthcare Data Management
Due to compliance hurdles and the daunting cost of updating legacy systems, the adoption of new IT solutions in healthcare tends to lag behind the cutting edge. As industries across the globe prepare for incoming disruptions from artificial intelligence (AI) technologies, healthcare data management continues to wrestle with defining best practices for the industry’s most foundational and pervasive data challenges.
Forward-looking healthcare organizations should prioritize establishing effective IT solutions for data management and prepare for an era of technological innovation poised to go into overdrive. This guide outlines the top healthcare data management challenges that organizations, from payers to care providers to biotech and life sciences companies, must deal with in the next one to two years.
- As industries prepare for the period of rapid technological transition driven by recent advancements in AI, the healthcare industry still struggles to identify standardized data management best practices.
- Although IT adoption moves slower in healthcare than in unregulated industries, today’s data management platforms offer viable and necessary solutions to healthcare’s persistent data challenges.
- Developing standardized best practices for healthcare data management requires adopting the necessary IT tools to address issues such as interoperability, data security, and insufficient analytical insights.
5 Top Healthcare Data Management Challenges
The pace of technological change has traditionally allowed regulated industries such as healthcare a slower adaptation period. However, this trend appears to be drawing to a close. The industry needs to resolve the remaining data management challenges that continue to hinder operations in most organizations. Here are the top five challenges.
Interoperability refers to the ability of systems, applications, and devices in an IT environment to connect, communicate, and coordinate. To achieve interoperability, organizations must enable data access, transmission, and inter-organizational collaboration.
In healthcare, seamless data exchange between different systems and applications directly impacts the quality of patient care, compliance with patient data regulations such as HIPAA and the HITECH Act, and provider data reliability. However, healthcare involves data created, formatted, and stored in many different systems and organizations such as:
Image Source: HealthIT.gov
- Clinics and private practices
- Medical imaging services
- Community health centers
Coordinating these data sources has proved difficult with single technology solutions such as electronic health records (EHRs). Modern data management platforms capable of abstracting master data automatically and standardizing patient data from multiple sources can help organizations develop effective interoperability.
2. Data Security and Privacy
Healthcare data breaches increased yearly from 2012 to 2021, doubling in the last three years. Per federal and state regulations, consequences of data breaches can be severe, including financial losses, legal liabilities, reputational damage, and – in serious cases – patient harm.
Many organizations have often justified their slow or nonexistent adoption of digital and cloud technologies as a means of insulating patient data from cyberattacks and data breaches. However, cloud-based data management systems have developed tools and best practices to ensure more secure data storage and transmission, compliant with applicable regulations. Today, organizations can ensure data security and privacy using tools such as:
- Access controls and advanced user-provisioning
- End-to-end encryption
- Data masking
- Automated audits for anomalous system behavior
3. Data Standardization
The greater real-time access care providers have to patients’ complete medical information – including EHRs, data from health information exchanges (HIEs), immunizations, allergies, and past procedures – the better care choices they can make. However, organizations store healthcare data in various formats and structures, making integration and analysis difficult without costly manual intervention.
Image Source: Onix-Systems.com
One emerging solution to this problem is the adoption of application programming interfaces (APIs) in healthcare databases. By integrating APIs to pull authorized data directly, data management platforms can standardize clinical data terminologies, such as SNOMED CT, LOINC, and ICD, to ensure consistent coding and documentation across different EHR systems.
4. Predictive Data Analytics
Businesses in all industries increasingly rely on data analytics to drive decision-making and keep operations within budgetary constraints. With access to necessary systems and sufficient standardization, predictive data analytics can help healthcare organizations identify trends, anticipate outcomes, and optimize care delivery.
However, healthcare has fallen behind in this field to the point that regulatory standards effectively make developing these capabilities a predicate for compliance. For example, Medicare’s Hospital Readmission Reduction Program incentivizes low patient readmission rates and penalizes high rates that indicate systematic oversight in discharge policies. Currently, 82% of hospitals fail to prevent foreseeable readmittance and incur these penalties. Alternatively, the potential benefits for organizations that develop analytics capabilities are ample:
- Early identification of high-risk patients
- Patient outcome tracking
- Improved resource allocation and lower overhead
5. Population Health Management
Average annual per capita healthcare costs in the U.S. are among the highest in the world. In 2021, total national spending on healthcare rose to $4.3 trillion – for an average of $12,900 per person – doubling per capita spending in countries of comparable GDP and per capita income. In the last 60 years, healthcare spending as a percentage of the national economy has risen from 5% in 1960 to 18% in 2021.
This year-over-year increase relative to the total size of the economy is unsustainable. Healthcare organizations must develop new strategies to tackle the problem. Healthcare costs and data management connect in the field of population health management. Population health management involves analyzing healthcare data to identify emerging health trends and risk factors, then taking community-level action to improve the health of the population.
These capabilities require processing substantially larger volumes of data than most healthcare IT systems handle. Going forward, organizations should prioritize implementing the necessary data management tools to enable actionable analytics for individual patient health and populations as new health issues develop.
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