A Comprehensive Outlook on Healthcare Data Management

Key Takeaways

  • Integration of AI in healthcare data management helps to make better decisions for acute care, which in turn improves patient outcomes.
  • Predictive analytics and data sharing programs such that of California can enhance population health and curb epidemic and pandemic-like situations.
  • Healthcare data management reduces hospital costs through remote patient care of chronically ill patients, thus controlling the cost of hospital readmissions.
  • Cloud provides automatic disaster recovery tools and regulatory compliance, making it easier to manage healthcare data.
  • Documentation errors, fragmented data, and privacy concerns pose major challenges for healthcare data management.

Data explosion is happening right now in the entire world. In every facet of our lives, data exists and there’s no end to it. Hence, data management is becoming a popular and much-wanted asset in today’s time. The healthcare sector is witnessing remarkable data transformation and people are willing to invest in healthcare data management for the betterment of population health.

As per a survey, by 2025 the compound annual growth rate of healthcare data is estimated to reach 36 percent. This is 6 percent faster than manufacturing, 10 percent faster than financial services, and 11 percent faster than the media industry. Let’s look at healthcare data management in detail.

5 Benefits of Healthcare Data Management

1. Improved Patient Outcome

Technicians and healthcare providers can make use of predictive analysis, artificial intelligence (AI), or machine learning (ML) to study healthcare data, for improving patient outcomes. Healthcare organizations can use predictive analysis to articulate the possibility of their patients developing medical conditions.

There’s a lot of healthcare data that can be garnered from sources such as EHR (Electronic Medical Records), biometrics, claims, etc. This data is put together to predict the likelihood of patients’ medical diseases. Further, as per a 2019 study, the integration of AI offers great synergy for complex decision-making in acute care. The use of AI algorithms to read disease patterns is useful for preventing adverse outcomes.

Through healthcare data management, physicians can identify which diseases can worsen. Prior knowledge of medical conditions helps physicians to take countermeasures against fatal or chronic diseases. Hence, improving patient outcomes. For example, healthcare data gives an idea of diabetes patients who are more likely to develop renal disease. So, according to the data, patients are given early preventive measures to improve their outcomes in the future.

2. Help to Keep Track of Population Health

Devices such as FitBits, Apple Watch, and healthcare software like telemedicine and EHR capture users’ data. This data is useful for controlling population health. Further, big data is used to address population health concerns.

Studying social determinants of health via big data helps to understand aspects beyond what’s recorded in a clinic. These include food insecurities, living conditions, domestic violence, mental health, etc. The community need index and social vulnerability index can help to target people who are in dire need of better health, thus improving population health.

To better population health via healthcare data management, California has taken certain steps. This is the first-of-its kind data sharing agreement that became available to sign in November 2022. The Medi-Cal Managed Care Plans will implement the CalAIM Population Health Management program in the coming month. The program reformulates the state’s approach to data collection to achieve optimal health outcomes and offer tailored services.

Further, healthcare data management can help to predict population health trends. For example, large consumption of alcohol in the USA will lead to liver diseases, resulting in death. Additionally, population health management is a way to curb epidemic and pandemic situations such as Covid-19.

3. Enhanced Patient Engagement

To receive quality healthcare services, patients need to participate in their wellness routine. As per the report by actium health, 50 percent of adults agree that their health has worsened since 2019, however, 83 percent of the people are ready to make changes in their health. Here, patients’ engagement with their care providers is essential to ameliorate their current health conditions.

According to the report, 38 percent feel that their doctor doesn’t listen to them; 35 percent don’t get enough time to discuss their health with doctors; and 44 percent don’t see a doctor enough. All of these reasons result in a decline in patient engagement. But, healthcare data management by providers can improve it. Health data helps to understand medical conditions. Based on this, health facilities can create patient profiles and send customized messages on how to best improve their ailment.

No-shows and cancellations hamper patient engagement, hence it’s important to understand the reasons for it. Consider sending out feedback forms and analyzing the data to form strategies. Improving the appointment scheduling process, sending alerts/reminders, and taking a follow-up can help to increase patient engagement.

4. Healthcare Cost Reduction

Healthcare executives depend heavily on data analytics as it alerts financial experts, clinicians, and administrative staff about threats before they happen. This allows them to make informed decisions. Predictive analytics tools help healthcare facilities to check cost savings, efficiency, and patient satisfaction.

Furthermore, predictive analytics helps to create risk scores based on biometric data, lab testing, patient data, and claims data. This information eventually assists providers to identify patients who are at a higher risk of contracting chronic diseases and reducing hospital readmissions after discharge. Prevention of hospital readmissions helps patients significantly cut down their healthcare costs.

Additionally, the supply chain provides the biggest opportunity to reduce healthcare costs. For example, optimizing the frequency of products based on patients’ needs and automating invoices or purchase orders.

5. Better Business Decisions

Healthcare facilities are relying on data-driven processes to make better business decisions. Patient behavior data is used to improve patient satisfaction through ways such as reducing wait times, appointing more nurses, and tweaking care processes based on how patients rate their healthcare experience.

Additionally, as discussed above, healthcare data helps to improve patient outcomes through seamless follow-ups and regular checkups. Scrutinizing cost data and claims helps clinics and hospitals to operate efficiently.

Furthermore, research and development data offers insights on new treatments and drugs, which makes it easier to decide whether to incorporate them into the care routine or not. All of these aspects help healthcare organizations to make better business decisions.

Healthcare Data Management Techniques

1. Decide a Storage Place

To understand where to store humongous healthcare data, it’s important to know the source of data. Healthcare staff use connected medical devices and software to collect gigabytes of patient data. This data has to go somewhere. Medical facilities use either on-premises, cloud, or hybrid data storage systems.

a. On-Premise

An on-premise data storage is a server installed in a medical facility. There’s complete control over software and hardware systems. Hospitals and clinics are responsible for data management, software updates, hardware maintenance, and technical glitches.

On-premise data storage is advantageous as there’s no need to share patient data with a third party. Information is saved on hard drives inside the facility.

b. Cloud

Cloud storage is offered by a third-party vendor and is maintained by them too. Healthcare data can be easily accessed online by patients and doctors. Cloud provides automatic disaster recovery tools and regulatory compliance. Cloud service providers (CSP) are required to comply with HIPAA (Health Insurance Portability and Accountability Act) rules.

According to information on hhs.gov, the covered entity or business associates and the CSP must enter into a HIPAA-compliant BAA (Business Associate Agreement). The CSP is contractually liable to meet the terms of the BAA and is directly liable for compliance with the requirements of the HIPAA regulations.

c. Hybrid

This is a unique storage option as it helps healthcare organizations to use the strong sides of both cloud and on-premise platforms. Hybrid data storage helps to build a secure and scalable platform.

However, it requires a high installation cost and has a complicated configuration. It complies with HIPAA, thus ensuring high data security and privacy.

2. Documentation

Within healthcare data management best practices, documentation can’t be overlooked. It’s wise to maintain multiple levels of documentation that will provide the full context of data. Documentation levels can be segregated into:

  • Project-level
  • Software used
  • File-level
  • Context

3. Naming and Catalogue Conventions

To use healthcare data, medical staff must be able to find it. For this reason, naming and catalog conventions are essential. Create a file system that is user-friendly, descriptive, and has standardized file names that are easy to locate in the future as well.

Design file formats that allow users to discover and search data in the long term. For example, for dates, the ideal format can be YYYY-MM-DD or YYYYMMDD. To list out time, it is best to use standardized 24-hour notation.

4. Robust Data Recovery Process

Data loss due to human errors is more prevalent as compared to data loss due to natural disasters or criminal activities. Healthcare facilities must be prepared for any data loss that can disrupt day-to-day operations. Hence, it is necessary to install a robust data backup and recovery process for effective healthcare data management.

Data backup and recovery process covers:

  • Servers: On-premise, cloud, and hybrid data storage systems must be backed up every day.
  • Endpoint devices: These include laptops, computers, workstations, mobile devices, etc. Individual hard drives of these devices need to be backed up and restored continuously.
  • SANs (Storage Area Networks): These encompass file storage, objects, and blocks that should be backed up and restored.

Challenges of Healthcare Data Management

Healthcare data is stored from varied sources and in different formats. Capturing accurate, clean, and comprehensive data is a challenge for healthcare organizations. The inability to bind data together can result in errors and incomplete information about a patient’s medical history and condition.

There are chances of data duplication if it is unstructured or stored in various places, and different versions (spreadsheets, video files, digital documents, etc.). Fragmented data is challenging to locate. If accurate data isn’t found then patients may have to repeat tests and diagnosis, resulting in additional healthcare costs.

Further, phishing and malware activities have haunted the healthcare sector, hence securing ePHI (Protected Health Information) is a major concern. The inability to do so can lead to data breaches and hampers patients’ privacy. Multi-factor authentication, biometrics, firewalls, data encryption, updated anti-virus, and regulatory compliance are followed to safeguard sensitive data.

To ensure the complete security and privacy of healthcare data, consider partnering with a healthcare software development company that will build robust data management systems. As they’re well versed in healthcare regulations, developing a compliant healthcare software solution is easy for them.

Arkenea is a healthcare software development company that can help you to create and design top-class software that matches your organization’s standards and full fills all your requirements. Get in touch with us to know more.