Health informatics is an emerging practice that continues to grow. There are numerous colleges today that offer degrees in Health information. The heart of health informatics is Health Data Management (HDM) of healthcare data, information, and knowledge for decision support by care providers, teaching hospitals, research centers, and pharmaceuticals and biotech companies. Healthcare Data Management is evolving and improving the delivery and support of medical treatments.
Making sense of healthcare data and managing outcomes of patients are driving the practice of healthcare today. Today, we can see this in the management of the world’s response to COVID. The response is very data-driven to determine decisions for an appropriate response to the virus worldwide.
What is Healthcare Data Management?
Healthcare Data Management (HDM) is the processing of managing the lifecycle of health data. Data is created, stored, organized, processed, archived, and destroyed. In addition, data is kept secured and protected to maintain a strict level of confidentiality, integrity and is only available to those who need access. Healthcare database management systems have the capability to do all of this and more such as analyzing disparate and diverse datasets from multiple internal and external sources to deliver operational and decision support to applications, devices, and people.
Healthcare Data Management is increasingly about digital data, on-premises, in the cloud, and out at the edge of the network for mobile and telehealth and medical devices and instrumentation. There are structured and unstructured data that has to be managed. Some organizations are starting to utilize a data warehouse for the volumes of data that have to be managed and analyzed in a Healthcare Data Management System (HDMS). These systems also include a Clinical Decision Support System (CDSS) that takes advantage of all the data stored to automate interpretation, care plans, and treatments for patients.
What is Healthcare Data Management?
Healthcare Data Management, also sometimes referred to as Digital Healthcare Data Management, is not limited to electronic health or medical records (EHRs or EMRs) but includes population health records, other clinical records for drug efficacy, or even medical instrumentation logs and RF-ID tags on various physical assets from beds to bedpans necessary for the supply chain management. Management of the data also includes all the operational and financial records spanning healthcare providers and payers – public such as national and state healthcare programs like Medicare and Medicaid and private insurers.
Challenges of Health Data Management
Challenges in Healthcare Data Management evolve around the enablement of people, both the provider of health care and the patient. The processes and technologies have to be aligned to the needs of all the stakeholders in the overall value chain for providing and receiving Healthcare. Some of the biggest challenges of Healthcare Data Management are:
- Security of the data. Data needs to be stored securely – confidential, integrity, and available to only those who should have access. Mandating that data be shared securely is the first step in improving outcomes and shifting to value-based care delivery and payment integrity model and away from our current inefficient fee-for-service model. This also helps protect patient data from unauthorized sources that may use the data for other purposes such as ransomware. Data must be protected according to US Health Insurance Portability and Accountability Act (HIPPA) compliance. The system has to be in compliance with government regulations for role-based access, encryption at rest and in transit. The system also has to be resilient and protected from cyber-attacks.
- Data integration. Integration of health data is important from various stakeholders, which includes the patient, provider, creditors, payers, and government. Integration and analyzing various healthcare data: clinical, operational, and financial data, including combining this data with external population health data and other social determinants of health, becomes valuable for public health data management.
- Catalog of datasets. All the various datasets from asset IDs and EMRs, Claims, EHRs, Pop Health Data, Accounts Receivables, and other sources are creating challenges with tagging and managing a rich set of metadata with proper ontologies and taxonomies for various elements of each dataset relative to the rest. Further, ingestion, replication, and combining data can result in duplication, errors, and other anomalies that must be identified and eliminated to avoid a wide range of problems, ranging from adverse drug reactions to payments fraud.
- Managing various data formats.
- Healthcare Data Management covers everything from the healthcare records in large legacy Hospital ERP applications like Cerner or EPIC and formats for medical imaging like DICOM encapsulating an image or video in JPEG or MPEG formats or claims submission EDI formats such as X.12 837. A healthcare data management system must be able to convert between various healthcare data formats. Maintaining data quality
Medical records have to be accurate. The patient record management system must have oversight when transforming medical records into accurate data. Many errors and omissions can occur that can cause harm to the patient.
Other challenges can be related to the technologies being used for the data. The database has to be scalable for all the data that is collected. Data has to be able to be consolidated from various technical platforms and sources. The Healthcare Provider Data Management and Hospital Data Management System have to meet all of these requirements. Cloud enterprise data warehouses and data marts can be viable solutions to help with these issues.
Benefits of Health Data Management
The benefits of Healthcare Data Management can be looked at from an elementary perspective. The better the data you have, the better decisions can be made, and improved outcomes can be achieved regarding the healthcare of patients. Besides the significant aspect of providing healthcare to help patients.
Some of the other benefits of Healthcare Data Management are:
- Health data analytics. It can be used to make predictions about patients’ health to enable better treatment and overall a better proactive approach to providing health care—the overall improvement of health outcomes for the patient and sometimes for the general public.
- Better alignment and communication. Communication improves patients, providers, and other stakeholders, especially with access to digital records. A comprehensive view of the patient can enable better collaboration between doctors. This is also helpful across geographic boundaries and countries.
- Improved patient engagement with healthcare. This includes improved visibility in the patient records by themselves to understand treatments, trends, and proactive care. The patient can easily access their health records anytime and anyplace when needed.
- Data-driven decisions. Historical data, real-time data, and other data can help improve provider and patient decision-making. Data can improve the diagnostic ability of both provider and patient instead of inaccurate guessing based on hunches.
- Integration with patient personal health-related activities. Physical activity, especially individual patient monitored activity with sensors, can be fed into the health record management system for improved treatments. Today, many mobile applications allow integrations or sharing of data from sensors or other applications with healthcare data management systems.
- Integration with emerging technologies. Improved integration with Artificial Intelligence to help diagnose illness without the need for a physical doctor visit—better integration with medical chatbots that use medical knowledge management systems integrated with health data knowledge for self-service.
Besides the challenges and benefits listed above, a high-quality, well-organized Healthcare Data Management solution can be achieved. Health data and management solutions support a wide range of use cases, including improved chronic disease management, accelerated clinical trials with more accurate recommendations, optimized use of provider resources, improved wellness programs, better alignment between payers and providers, thereby reducing the time and expenses involved in back-and-forth struggles with down-coding versus upcoding.
HDM decision support systems
Healthcare data enables decision support for all stakeholders, provider to patient. Data is everywhere and needs to be available in real-time for timely decision support. With integrated secure collaborative systems, data can be used to measure anything Healthcare related, manage decisions and monetize actions. Healthcare service catalogs of information can be created by various providers and utilized with factual, real-time data to determine the availability of services and products.
So many decisions can be made using a healthcare data warehouse of various information by various stakeholders across the healthcare provider’s organization or for the patient themselves. Supply chains for physical healthcare space, medicine, specialty care, etc., can all be managed with a healthcare database system, especially an integrated shared secure system between providers.
Emerging technologies such as artificial intelligence, machine learning, and others can take advantage of social, mobile, and cloud platforms with the utilization of heath data to support numerous use cases. Clinical decision support systems can analyze evidence-based data collected in a health care management system at any point of care, either routine or emergency care.
When a healthcare provider can do their job better, and the patient has better knowledge about care, all are great for both. Healthcare data management systems combined with the expert opinion of care providers will increase the efficiency and effectiveness of health care.
Conclusion
Healthcare fraud affects everyone, from patients to providers. Healthcare Data Management solutions can help reduce these challenges. Data quality management in healthcare helps protect billing, identity theft, forgery, medicine abuse, and many other challenges. The integrity of the healthcare data management solutions can help save the healthcare community financially and individual patients. As healthcare systems and databases become more securely integrated and shared among payers and providers, the better the transparency and support of enforcement of rules and regulations can be done.
Recently, under the Cures Act, healthcare payers and providers have been instructed to share more of their data and have recommended updated formats for sharing data in various formats in a new version of HL7 called FHIR (Fast Healthcare Interoperability Resources). The key is that data be protected according to HIPAA compliance and with need-to-use guidance under Meaningful Use guidelines. Central to HIPAA compliance and Meaningful Use is to maintain encryption of all data at rest with AES-256 bit encryption, use SSL and encryption for data in transit, and combine granular masking and authorization of data with role-based access multi-factor authentication. Further, as data is moved to the cloud or accessible from outside the organization, intrinsic cloud security mechanisms in the three public cloud platforms (AWS, Azure, and GCP) should also be leveraged.
In addition to the above, medical errors are a leading cause of death in the United States. Some of these errors are caused by communication problems between providers and patients, lack of information for prescribing decisions, and poor data documentation. Reducing these types of medical errors can be done with improved healthcare data management.
As mentioned earlier, we all can see the effect of Healthcare Data Management on how the world exchanges data for responding to COVID. Data is coordinated, collaborated, and shared quickly. Based on the data, experts worldwide can make informed decisions on how to respond to the virus based on many factors such as their economy and other unique constraints. The general public, like never before, pays attention to health data for individual decisions about their options for care.
Healthcare Data Management has many challenges and benefits. Healthcare Data Management companies are rapidly improving to meet today’s and tomorrow’s challenges. The benefits clearly outweigh the challenges. Tomorrow looks bright with the enablement of new innovative technologies to support healthcare for both providers and patients.