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How Is Data Used in Healthcare: Healthcare Organization Data Management

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The growing amounts of data circulating in the healthcare industry have provoked the need to develop and adopt big data health services. That affects how data is used in healthcare and requires a different approach to building an efficient data strategy for medical organizations. 

So how do you implement digital transformation on your way to creating a data-centric healthcare system?

What Is a Data Strategy in Healthcare?

A data strategy implies a plan that considers rules, processes, technology and people that manage data within an organization. In other words, a data strategy helps define how an organization collects, stores, shares and uses data. A long-term data strategy is beneficial for all types and sizes of businesses across various industries, including healthcare.

While adopting a data strategy in a healthcare organization allows for better data management, strong system security and healthcare predictive analytics implementation, not all companies are already using these benefits. According to Deloitte, only:

  • 1 in 5 healthcare organizations has adopted a data strategy
  • 1 in 3 does not know about their expenses for data analytics
  • 1 in 4 does not have a data governance model implemented
Lack of effective healthcare organization data management strategies
Figure 1. Lack of effective data management strategies in healthcare

Of course, designing and implementing a data strategy in an organization can be a challenge. But why is it essential for healthcare facilities to do that anyway?

The Importance of a Sustainable Data Strategy in Healthcare

With the constant evolution of technology and changes in the needs of healthcare organizations, building a data strategy is a must to keep your data management consistent and compliant with regulations. Also, an effective strategy allows for keeping data clear, sorted, unbiased and convenient for further access.

Besides, the importance of a data strategy is proved by the fact that steps are taken toward defining a centralized outline for data management in healthcare at the governmental level. For example, the UK’s government issued an official social care data strategy called “Data saves lives” in July 2022, the goals of which are to:

  1. Improve trust in healthcare systems’ use of data
  2. Provide organizations and professionals with the data they need to give care
  3. Improve data for adult social care
  4. Back up decision-making with data
  5. Provide access to data for diagnostics and treatment development
  6. Create partnerships for data-driven innovations
  7. Build technical infrastructure based on data

With this, a data strategy is crucial for today’s healthcare organizations because it brings the services to the next level. A data strategy can transform the organization and its performance from short-term improvements like enhancing internal decision-making to long-term ones like growing patient trust and increasing revenue.

Figure 2. Data value lifecycle with a data strategy in healthcare
Figure 2. Data value lifecycle with a data strategy

What Sources of Data in Healthcare Are There?

First, before looking for the best ways to manage data as a part of a data strategy, it is important to clarify where healthcare data comes from. Overall, there are three areas of sources of data in healthcare: research and trials, patient examination and treatment, and administrative data. 

1. Clinical Research and Trials

Primary data is obtained from the research process and then studied to examine certain conditions or potential treatments. Research can also provide valuable datasets that shape statistics on mortality rates, predispositions to certain conditions among people of various demographics and backgrounds, etc.

These sources of data in healthcare and the findings they help obtain are essential for the overall development of the field. Then, the results of the studies and trials, along with statistics, allow for curated data sources for informed decision-making. Clinicians would further refer to these as they provide care throughout their medical practice.

2. Patient Examination and Treatment

Patient data can come either from electronic records or wearables and personal devices. Electronic health records (EHRs) are a powerful source of information about each patient and their medical history. Among all, EHRs may contain the following:

  • lab and test results
  • treatment history
  • records of previous diagnoses
  • scans and images
  • treatment plans
  • vaccination dates

Storing patient data online is highly convenient — patients and clinicians can access it anytime and from multiple locations and devices. Besides, exchanging data between departments and facilities is easy and fast with EHRs.

Patient health data sources
Figure 3. Patient health data sources

However, the fact that EHRs contain detailed information about patients’ health makes them a sensitive data source. The mHealth data storage and the use of electronic health records are regulated by HIPAA (in the US) and GDPR (in the EU). Because of its sensitivity, this data often becomes the target of cybercriminals. 

3. Administrative Data

Administrative data implies any information on the healthcare facility’s operations regarding its service. So a patient’s visits, bills, and insurance information are logged into the healthcare enterprise support solutions and are considered sources of administrative data. 

While convenient for reporting and bookkeeping, this data contains personal data, which poses identity theft and financial fraud threats.

Advantages of Implementing a Data Strategy in Healthcare

Naturally, your organization gains a significant competitive advantage if the data strategy you adopt aligns with your business goals. In detail, a sustainable strategy of healthcare organization data management, including its collection, usage, storing and sharing, leads to the following:

  • Process optimization — your organization can follow a clear flow for operations with data that is developed according to the goals and needs of your particular business.
  • Informed decision-making — decisions across all areas of processes, from administrative to in-patient, are based on data and can be supported with evidence. 
  • Efficient operations — data management systems allow automation of various related processes, from data collection to analytics.
  • Increased revenue — digital data management helps distribute resources properly while minimizing errors caused by data gaps, therefore leading to better patient care.
  • Higher customer satisfaction — a data strategy means that your processes are clearly defined, and you know what to do and when, so you can provide high-quality service.

Overall, a data strategy shapes a clear and comprehensive view of how data moves within your organization, benefiting all departments.

Best Practices of Healthcare Organization Data Management

Here are four recommendations for building an effective data strategy for your facility:

1. Switch to Digital and Electronic

While the majority of hospitals in the US have already switched to EHRs, they still do not cover the entire flow of patient data. Often, healthcare organizations still generate patient records in other forms. But why does this matter for data strategy in healthcare?

The use of technology and healthcare enterprise support solutions provides excellent opportunities for advanced mHealth data storage, management and exchange. Since electronic health records make up an extensive system, it allows automated data logging, sorting and filtering for a clear and quick view of patient data.

Besides, developing a system that covers all areas of processes allows various departments and medical staff to find and access the information they need fast. That translates into practitioners, lab technicians, nurses, and other employees at the facility being more effective and providing better service.

2. Interoperability Is Key

While adopting EHRs and other data management technology to a healthcare facility, it is crucial to ensure that data is consistent or comprehensive at all stages and across all users and machines. And this outcome derives from data standardization and interoperability. 

Because medical and patient data can come from various devices, machines, facilities and locations, the chance of data misinterpretation or corruption remains high. 

In the long run, poor interoperability leads to stalls and issues in the facility’s operations and healthcare organization data management. Moreover, misinterpretation of data because of the lack of interoperability increases the chances of medical errors during patient treatments.

Managing data interoperability in healthcare
Figure 4. Managing data interoperability in healthcare

3. Protect Sensitive Data

As already mentioned, large data volumes organizations use and keep in mHealth data storage are sensitive patient information. In 2021, almost half of the data breaches targeted such information. So since this data is susceptible to security breaches, it should be treated accordingly. 

Protecting sensitive information as a part of your data strategy implies following regulations like HIPAA and GDPR. Similarly, establishing policies for data protection for users of applications and devices is another measure to include in the strategy for data security. Other practices of sensitive data protection are secure wireless networks, data encryption and staff training.

4. Adopt Real-Time Data Streaming and Analytics

With the growth of the Internet of Things (IoT) and wearables, personal devices are becoming a reliable source of patient data. That results in meaningful data like heart rate, oxygen level, and other information on patient health.

But to ensure that this data on patient well-being reaches physicians on time and can be acted upon, it must be shared in real-time. That is when data streaming technology comes in handy.

Then, AI and ML algorithms process the incoming streams and analyze the data as it reaches the healthcare enterprise support solutions. The data is filtered to remove unnecessary information or duplicates and log valuable only. That leads to sets of ready-to-use, insightful data that is easy to manage. 

Challenges in Big Data Health Services

Digital transformation can be tough for an organization because there are still some challenges and gaps in switching to digital. Here are some of the obstacles you may come across when designing and implementing a data strategy:

  • HIPAA compliance — the regulations require cybersecurity measures to protect patient information in EHRs that also need to align with other technology your system uses.
  • Wireless access security — because of how convenient and limitless access to digital data is, wireless networks can become a weak point and pose a security threat.
  • Data standardization — sharing data between organizations outside of your facility may end up causing a conflict in data formats, leading to the inability to interpret data.
  • Clinical and administrative data integration — there must be no gaps between clinical and administrative data management for a comprehensive, well-operating system.
  • Legacy system integration — integrating an old data management system into a new infrastructure while ensuring regulatory compliance can be a complex challenge.

Of course, the best strategies to outline healthcare organization data management are still under development, and the most effective solutions are not always obvious or easy to implement. But having an experienced team developing and setting up data management systems is key to minimizing risks and challenges on your way to digital transformation.


Effective healthcare organization data management is the goal of a data strategy in the healthcare sector. But to make big data services beneficial for your company, you must build them depending on your business goals and needs. If you are thinking of building a custom solution, contact us, and we will develop a comprehensive system for your medical business.

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