The medical sector aims to provide high-quality care and treatment more effectively. Thus, healthcare organizations should engage patients differently than typical in-person doctor consultations. Statista reports that in the United States, over 50% of researched patients have electronic health records (EHRs), and 44% can access them if needed. It means that by giving patients autonomy and the chance to have their records digitally, healthcare data management is essential, mainly due to the increased prevalence of virtual care.
Health information is often complicated and high-dimensional, from simple data like patient demographics to specific medical images and other massive data. That is where proper healthcare data management comes in handy, covering multiple aspects like information collection, sharing, etc. Although these tasks are far from easy because of the need to follow various standards like ICD-11, HL7, or SNOMED, the outcomes are incredible: all your health systems and apps will communicate using the same “language.”
Below, we will analyze all the ins and outs of quality healthcare data management, such as pros, challenges, and tips to address them, as well as things to consider when looking for data storage. So, take your seat and enjoy!
Table of Contents
Fundamental Pillars Holding Up Healthcare Data Management
What does health data management refer to? It means that healthcare organizations can acquire, store, organize, and use the required information from different clinical sources. These include electronic health records, lab systems, pharmacies, etc. However, there are also non-clinical sources involving public databases or Internet of Medical Things (IoMT) devices.
Effective healthcare data management depends on collecting structured or unstructured information from the sources above, standardizing its usage, and maintaining its various types. For instance, the latter may cover patient data, medication history, clinical records, studies, research works, medical images, or administrative information.
So, below are the most crucial pillars that hold up efficient data management in healthcare.
Aggregating Data
This pillar means collecting the relevant information from different sources, like EHRs, electronic medical records (EMRs), medical wearable devices, lab test results, and patient-reported data.
Storing Data
Medical information storage should be secure and easily accessible. Healthcare organizations traditionally use specific databases, data warehouses, and cloud-native storage tools to keep such information safe. These solutions help guarantee that digital files are always available and confidential and can be integrated seamlessly into other platforms if needed.
Integrating Data
Here, collecting data from numerous sources to further create a complex and unified view is the core. The most typical example is data combination conducted by various departments within the same healthcare facility. Medical companies can also gather data from external sources, including health information exchanges (HIEs) and public databases.
Assessing Data Quality
The information healthcare organizations collect and store should be of the highest quality. So, such data must pass through detailed quality checks, cleansing operations, and scrupulous validation processes. These activities allow for quickly identifying and correcting existing errors and inconsistencies. After all, the approved quality results in better governance accuracy and makes medical data consistent and complete.
Ensuring Data Security
The particular pillar of healthcare data management requires implementing powerful security measures and following industry best practices and regulations, like HIPAA or GDPR. With encryption protocols, access controls, and regular system audits, your medical organization can ensure excellent healthcare data protection and prevent it from leaks, breaches, and misuses.
Analyzing Data
Medical information analysis involves extracting valuable insights and gaining knowledge from aggregated healthcare data. Your practice can utilize different analytical techniques to do that properly, such as:
- Data mining
- Machine learning (ML) algorithms
- Artificial intelligence (AI) tech
- Statistical analysis
However, you should remember that predictive analytics in the healthcare domain can assist in determining patterns, trends, and tendencies, which is essential for better decision-making, planning initiatives, and further research.
Sharing Data
Exchanging medical data through many critical health systems and stakeholders, like healthcare practices, hospitals, labs, clinics, etc., is always advantageous. This process can guarantee that all involved parties can access and integrate the required information while focusing on patient privacy and security because of the proper access controls.
When adopting a unified data model focusing on the pillars above, healthcare systems can assist medical companies in cleaning all sources, driving collaboration, and creating a solid, future-proofed ground. In turn, that improves the overall interoperability between systems and accelerates innovation.
Explore the power of secure healthcare data management. Contact us to partner with industry experts committed to elevating your data solutions!
7 Key Advantages of Effective Healthcare Data Management
The proper healthcare data management may bring outstanding benefits for medical practices, their personnel, and patients. For instance, the most crucial benefits involve:
Better Patient Care
Healthcare companies can access complex and accurate patient-related data with robust health data and management solutions. Such data can cover medical history, prescriptions, allergies, lab results, etc. That greatly enhances decision-making processes in clinics, promotes exact diagnosis, and empowers personalization regarding treatment plans. All of the above is possible because of utilizing unified information as a single source of truth. This approach helps establish effective coordination between many healthcare practices in a patient’s treatment.
Improved Patient Data Safety
Modern healthcare data management software allows for reducing the risk of potential medical errors and eliminating adverse scenarios. When managing and updating the particular tools regularly, healthcare practices get constant access to complete and actual patient data such as medication records. That may assist in identifying patients, preventing medication errors, and avoiding allergic threats.
Increased Operational Efficiency
An advanced healthcare data management system is a valuable asset for streamlining relevant processes and increasing the efficiency of healthcare providers and their staff. For example, using EHRs and leveraging digital documentation means the end of paper files. That results in reduced administrative burden, cost savings, and minimization of lost or misplaced records.
After all, automated operations like data entry, doctor appointment scheduling, and billing are vital for higher workflow efficiency, enabling providers to ensure high-quality care.
Population Health Analysis
The correct healthcare data management creates a solid basis for detailed analysis of collected and processed information. That is crucial for identifying health trends and tendencies, exploring disease patterns, and gaining knowledge about health-related risks. Such invaluable insights assist healthcare practices in developing preventive strategies, adopting public health interventions, and allocating required resources for regions, countries, or communities in need.
Thorough Clinical Research
With continuous access to complex multiple datasets used for analysis and science, healthcare practices can strengthen their clinical research activities. Therefore, healthcare data management platforms enable researchers to utilize necessary information to define future study participants and perform epidemiological research. Meanwhile, these solutions also allow for assessing treatment outcomes and creating evidence-based guides and industry protocols.
Strategic Planning & Healthcare Policies
Private medical companies and public organizations have a goal to enhance policy development processes and provide a more strategic allocation of resources by leveraging the collected data. At the same time, the particular approach helps improve healthcare planning at different regional, national, and worldwide levels and makes it more scalable.
The specific medical databases, like those focused on disease prevalence, healthcare delivery, and patient outcomes, may promote more informed policy decisions. Besides, they contribute to thorough public health strategies, better resource allocation related to healthcare infrastructure establishment, and more productive staff. After all, healthcare data management can ensure timely interventions for various chronic conditions or global pandemics such as coronavirus.
HIPAA-Compliance & Data Privacy
When it comes to data management, healthcare is the most challenging industry to maintain information security and privacy. Fortunately, most cloud-native tools can guarantee compliance with the current rules and regulations in the US, including HIPAA.
Being HIPAA-compliant means that medical organizations provide robust protection of patient data, maintain their confidentiality, and avoid data breaches caused by unauthorized access. So, by adopting effective security practices, encrypting information, and conducting audit trails, healthcare practices can better process and manage their datasets in private data centers.
Thus, for medical providers healthcare data management is vital to improve patient care, strengthen data safety, increase operational efficiency, and promote data-driven decision-making. All of that leads to the best possible health outcomes and allows for delivering quality medical services.
Main Challenges of Healthcare Data Management
Years before, healthcare data management software assisted in simple paper-based tracking. But its constant evolution and digitization promote the appearance of new trends. At the same time, the emerging healthcare management issues and solutions to them lead to larger data volumes. The latter also requires transformation into digital formats and integration with software tools. Thus, below are the most critical challenges related to exchanging health data.
Fragmented Information
Utilizing health tech may provoke medical information vulnerabilities that cover various types of structured data. Healthcare organizations can integrate this data into their information systems or save the necessary images and videos into specialized digital formats (like DICOM), scanned files, etc. Besides, duplicating, storing, and collecting healthcare data many times in different formats across medical practices, public organizations, pharma and insurance companies, and patients is incredibly daunting for all stakeholders.
Data Changes
Unfortunately, continuous changes in healthcare information are among the most tremendous organizational issues in the industry. That is because various social, professional, and even geographical factors can impact patients’ lives and health conditions. They must pass multiple tests and deal with new treatment types repeatedly. After all, modern healthcare data management platforms offering advanced telehealth and other patient engagement features contribute to creating new types of medical information.
Industry Regulations
Since healthcare data consists of sensitive patient-related materials, such information is prone to relevant breaches and leaks. Although medical organizations must follow some industry standards established by a government or other legislative provisions, ensuring the proper data quality is challenging for most healthcare companies. They often find it difficult to conduct quality audits and satisfy each regulatory requirement. Such a situation limits resources crucial for processing diverse data from multiple healthcare providers that can benefit patient outcomes.
Information Overload
Healthcare data management software may sometimes be overwhelmed with tons of medical data. Therefore, healthcare practices of different types and sizes should invest in a robust IT security infrastructure. That is because adequate health software ecosystems with effective data governance allow medical organizations to avoid overload risks and eliminate severe financial and reputational losses.
Ultimately, the good news is that you can address all the challenges above. Let’s move to the next section and learn how to do that correctly.
Delve into the realm of claims analytics and discover how it is reshaping healthcare. From addressing denied claims to unlocking untapped potential, our blog covers it all!
Tips to Address Challenges of Healthcare Data Management
Managing healthcare data is complex, covering many essential processes such as data storing, entering, processing, accessing, and securing. However, with advanced technologies like AI and predictive modeling, medical companies can make more informed solutions and streamline their business operations. But what are the other ways to solve healthcare data management issues?
Utilize Data Entry Techs
Today’s healthcare organizations implement technologies for data entry, which is crucial for its aggregation from different health systems and other resources despite the formats. Moreover, all the collected information is then stored in a central repository. A great example of information entry techs is an IoT wearable device that allows for monitoring patient’s conditions remotely from the comfort of their homes.
Besides, healthcare providers can use customer relationship management (CRM) solutions to analyze patient needs profoundly and enhance their treatment experience. Meanwhile, optical character recognition (OCR) techs will assist in digitizing texts and reducing manual errors.
Ensure Proper Access to Data Storage
Instead of utilizing typical on-premise data storage platforms, modern healthcare providers can benefit from innovative cloud-based solutions. With cloud healthcare data management tools, organizations get flexible data storage capacity at a relatively low maintenance cost. They can increase and reduce the required space without investing in new servers.
Besides, such tools enable them to adopt hybrid or remote work and use third-party apps seamlessly. Last but not least, cloud-based solutions are less risky regarding medical data breaches than on-premise servers. That is because this information is stored in different locations, which makes it better protected.
Leverage AI & ML for Fraud Detection
AI & ML techs enable medical practices to provide optimal healthcare data security. Adopting such technologies allows organizations to control access effectively and integrate data transfer or storage encryption. At the same time, healthcare companies can benefit from AI- and ML-based fraud detection and prevention tools. The latter can monitor various suspicious activities and guarantee their close inspection. Finally, data-driven fraud detection is always more powerful and cost-efficient than traditional systems.
Implement Scalable Data Architectures
The digital era forces healthcare data management tools to generate and process exponential data volumes each second and mine this information to get valuable insights. But it also means that innovations and techs available in the market will contribute to the increased information output. That is when cloud computing comes in handy, meeting medical organizations’ existing and future demands. With cloud-based platforms, healthcare practices can have scalable data architectures without investing in new hardware.
Use Predictive Modeling and Information Intelligence
Nowadays, healthcare practitioners integrate multiple analytical tools and approaches to appropriately analyze and understand the collected information. That helps them derive the correct value. Meanwhile, with data intelligence, medical providers enhance treatment outcomes, realize health threats early, and promote personalized care.
By utilizing AI-based predictive modeling techs, healthcare organizations can proactively process patient records and other critical health-related data to detect chronic diseases. So they get a chance to prevent relevant high-risk medical events. Lastly, predictive modeling techs may reduce costs on internal care processes and adjust treatment plans based on the patient’s medical needs.
Explore the intricate world of healthcare data collection in our article, from understanding its importance to uncovering tools, methods, and challenges!
Things to Consider When Choosing Healthcare Data Storage
Besides discussing the key benefits and challenges regarding healthcare data management software, each medical practice should also consider some critical points about data storage:
- Scalability. Today, unstructured information, like magnetic resonance imaging (MRI), computed tomography (CT) scans, X-ray, and positron emission tomography (PET) scans, show the fastest growth among all types of healthcare data. Since Petabytes of such files can exist, medical practices must implement a scalable, cost-effective storage platform.
- Compliance. HIPAA-compliant healthcare data storage should meet some criteria, such as those following other established industry standards and rules. For instance, such regulations must protect information with specific Role-Based Access Controls (RBAC). At the same time, they also cover implementing audit trail logging, encrypting data at rest, and using Secure Sockets Layer (SSL) to transmit information. After all, the appropriate audit of the primary management procedures is essential, too.
- Vendor-neutral archive (VNA). With VNA, medical providers obtain a single interface for numerous healthcare data platforms. Thus, consolidating different types of healthcare data into one central storage becomes much more accessible. The particular storage will offer a unified view of the available patient records. When selecting the right platform for your business, you should look at the tools offering VNA integration.
- Information resiliency. Creating an effective healthcare data management strategy requires preventive measures to avoid cyberattacks. Since patient information is often a target for hackers, healthcare providers should minimize its accidental loss or deletion risks. So, an up-to-date storage platform must ensure redundancy, data replication, file backup, and erasure coding necessary for distributing fragments of information through many nodes.
Ultimately, if you select a scalable, HIPAA-compliant healthcare data storage that brings VNA integration and data resiliency, your medical practice will benefit greatly over time.
Discover the world of healthcare data warehousing and learn about the architecture, benefits, key features, and use cases that define this revolutionary tech!
How SPsoft Helps with Efficient and Secure Data Management in Healthcare
SPsoft is among the leading companies that assist health systems in moving beyond the current point solutions and fundamental interoperability to entirely data-driven operations. Our experts have significant experience building scalable platforms, leveraging the potential of innovative techs, and promoting digital transformation in the healthcare domain.
With SPsoft, you can:
- Establish the appropriate level of security and interoperability between various systems. These may include legacy, mission-critical, and many others.
- Aggregate and unify the necessary information from numerous sources.
- Make medical data usable by any application and within specific integrated workflows.
- Harmonize health data to provide its synchronization through each particular system and healthcare setting.
Thus, our skilled team may help healthcare organizations with data collection, standardization, monitoring, and analytics. In turn, unifying patient-related data across different systems and medical providers will enhance their clinical, financial, and operational results. After all, data quality management in healthcare benefits all stakeholders. That involves not only practices, patients, and their families but also payers, employers, and even pharma companies.
Final Thoughts
Healthcare data management is the core of the performance of EHR and EMR systems. It allows healthcare practices to enhance care and reduce medical errors while strengthening integration with other platforms. Besides, modern health data and management solutions regard such communication as the highest priority, especially when developing different mHealth apps for patients and medical organizations. So, these applications can collect and retrieve data seamlessly, delivering high-quality care and ensuring proper access to patients’ information.
At the same time, innovative data platforms assist healthcare companies in creating unified patient records. That allows for bringing all patient-related information together, covering data you can find beyond your healthcare systems. Finally, by incorporating advanced analytics, ML algorithms, and integrated workflows, your healthcare organization will obtain invaluable insights to achieve the desired business and medical goals.
Let SPsoft experts streamline your healthcare data significantly, guaranteeing industry compliance and efficiency. Your journey starts here!
FAQ
What is provider data management in healthcare?
Provider data management in the medical sector aims not only to organize healthcare data but also to integrate it and provide further analysis. That is crucial for improving patient treatment and deriving insights to enhance health outcomes. Provider data can include patient demographics, doctor notes, lab test results, imaging files (like x-rays, CT, and MRI), doctor prescriptions, provider-related information, and many more.
What is healthcare data management?
Healthcare data management, also known as Health Information Management, means a systematic approach to organizing data management in medical organizations. The relevant healthcare information is stored digitally in electronic health records (EHRs), electronic medical records (EMRs), etc. With proper healthcare data management, medical practices can gain valuable insights, improve patient outcomes, and protect confidentiality.
