Managing healthcare data is becoming increasingly complex. Data generated by providers is increasing exponentially, and the widespread adoption of electronic health records makes it possible for providers to generate several gigabytes of information every month.
This situation poses several challenges that make it imperative for practitioners to reorganize their data repositories.
Effective health data management makes it possible to use the data for analysis, forecasting, and effective decision making. However, to reap these benefits, providers need to understand how to overcome the following challenges.
1. Data Silos
As data processing in many practices and organizations evolved, different departments developed their systems for processing data. Today, virtually all departments from admin to billing, treatment room, lab, and pharmacy now use information systems.
These departments, however, seldom store and retrieve information in a central database. They have data stored in spreadsheets, desktop databases, image files, scanned paper documents, or special formats like those used for MRI scan results.
Unfortunately, data that can serve as input for smart decision-making and patient care improvement is fragmented in different data repositories. This situation leads to data duplication, inconsistencies, and poor integration.
The best way to overcome this challenge is to work towards integration and centralization using an enterprise resource planning approach that encourages using a single database with multiple interfaces that can serve each department’s needs.
2. Complying With Regulatory Requirements
Poorly handled medical data can lead to loss of life. Patient data is quite sensitive, and providers must manage it in compliance with regulations like HIPAA.
One of the significant challenges health data managers have is how to handle data in legacy systems. When old systems are retired, their data becomes difficult to access, especially when the system’s subscription has been stopped.
Consequently, when requests for data come from auditors to meet regulatory requirements, they are challenging to meet.
One of the effective ways to overcome this challenge is to create an active data archive. The archive will store all data from legacy systems and make it easy to retrieve information without delay.
3. Frequent Data Changes
Patient and other medical data are always changing. Names, addresses, patient conditions, physicians may need modification at any time. Changes in data without prompt updates can make data incomplete, inaccurate, or outdated quickly.
Patients’ state of health also changes over time, requiring various tests, medication, and treatment plans. Similarly, medications and their indications are continually evolving. And the methods of administering healthcare keep changing. These changes compel a constant review of data management methods to improve the quality of care and treatment outcomes continuously.
4. Data Growth
Digitization of processes and workflow is a blessing to both providers and patients. But with massive digitization comes a rapid increase in the volume of data generated daily in all departments.
While one can manage data created and stored as text in databases and images stored in compressible image files with relative ease, other unstructured data is not as convenient. Data coming in from CT scans, MRI, and X-ray requires a more scalable data storage solution.
Cloud-based storage is essential for these large image-based data. Such data must be managed by reliable cloud storage providers who will meet all regulatory requirements.
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