Data governance in healthcare: a comprehensive strategy

Data governance in healthcare: a comprehensive strategy

May 17, 2022

Inga Shugalo

Healthcare Analyst

The global healthcare analytics market size is growing exponentially and is projected by Fortune Business Insights to reach USD 80.21 billion by 2026. Complex analytical solutions which require terabytes of data to support life-saving clinical decisions have become an essential part of the healthcare industry’s toolkit. At the same time, due to the digitalization of healthcare, the volume of healthcare data reached a historical maximum in 2021, creating a number of challenges for providers. 

How should healthcare providers store all this data? How can they protect this much sensitive data while using it to the advantage of patients and themselves? The answer to most of these questions lies in the realm of data governance. 

Tapping into healthcare software development services, healthcare providers often overlook the practices essential for analytics’ effective outcomes. Even the best analytical solutions won’t provide reliable insights when running on poor quality data. Thus, to make the most out of your innovative data analytics software, we recommend establishing a data governance strategy beforehand to ensure both data quality and protection.

What is data governance in healthcare

Data governance is a set of practices that ensures safe and effective usage of data. The healthcare industry handles disparate data which includes patients’ personal details and health records, test results, billing and insurance information, MRIs, X-rays and EKGs, medication prescriptions, and other medical information. Due to its sensitivity, this data should be protected at each stage of the data lifecycle and processed correctly in order to be utilized for business and clinical decisions.

Data Lifecycle by AHIMA

Information governance vs data governance vs data management

Given the high volume and varied types of data in healthcare, there is no surprise it can be handled in a multitude of ways. It’s often hard to draw a line between them, as they may overlap, involve related processes, or be carried out by the same specialists. To avoid confusion, refer to the table below which explains the distinction among the most frequently used data-related terms.   

Information governance

The Information Governance Initiative (IGI) defines information governance as activities and technologies that organizations employ to maximize the value of their information while minimizing associated risks and costs. It usually deals with already processed data, like a list of medical recommendations based on test results.

Data governance

Data governance is part of a larger information governance strategy, mostly carried out by an organization’s IT department, and deals with individual pieces of digital data as well as their sources. It encompasses all activities aimed at keeping data reliable, structured, accessible, and protected.

Data management 

Data management is a combination of actions performed on data. It is regulated by the data governance principles. For example, patients’ personal and insurance information, test results, medication prescriptions, etc. are collected, processed, and disposed of according to the guidelines.

While the distinctions between these three notions may appear miniscule for a common healthcare worker, it’s crucial that medical data professionals can tell these concepts apart.

Benefits of data governance in healthcare

Health data stands out from other data types in many ways, but the most important one is that human lives depend on its quality, security, and accessibility to healthcare professionals. This makes data governance in healthcare crucial for a number of reasons.

1. Better clinical and business decisions

In order for analytics to draw meaningful insights from data, it should meet a number of requirements. Otherwise, medical analytics results won’t show a realistic picture that decision-makers can rely on while developing care plans or business strategies.

According to The American Health Information Management Association and other reputable sources, quality healthcare data should be:

  • Accurate, or up to date and free of errors
  • Consistent, with elements coming from the same source formatted in the same way
  • Reliable, or coming from verified sources
  • Comprehensive, with all required elements clearly defined and present
  • Precise, or having the proper level of details and collected in a particular format
  • Relevant to the purpose it was collected for

 

Data governance helps ensure that all the data a healthcare organization handles fits the above criteria. It is achieved through the combined effort of software that formats and structures data from the point of entry and employees that oversee and tune up these tools and verify data sources when needed. Adhering to the quality requirements, the data can be processed right away, without the additional structuring and verification.

2. Enhanced communication across departments

One of the major problems in the healthcare industry is data silos. Patients are forced to provide their information again and again during visits to different specialists, for this data to never leave a particular doctor’s office. This established practice is not only tiresome but also increases the risk of typos, errors, and information misplacements. What is more, it wastes the time of medical professionals that could’ve been dedicated to their patients. 

Data standardization, which is an integral part of the data governance framework, can solve this problem. It implies a set of rules defining how data should be collected and from what sources, which makes it suitable for sharing between departments and healthcare organizations. That is why the Office of the National Coordinator for Health Information Technology (ONC) has set the requirements for structured health information used for care workflows within and between institutions.

Data included in USCDI

3. Data protection

Most data in healthcare falls under either PII (personal identifiable information) or PHI (protected health information). According to a recent PEW survey, nearly 62% of patients are concerned about the safety of their information, while Statista’s 2022 survey revealed that 73% are willing to share it with particular organizations. To address both these sentiments, healthcare providers should guarantee data security or risk losing their reputation and funding. As of now, data breaches cost the healthcare industry more than any other.

Data breach costs according to IBM

Data governance strictly regulates the ways data can be stored and transferred. Its guidelines permit healthcare organizations to use software with appropriate levels of security for data processing and includes strict data handling rules for employees.

4. Improved regulatory compliance

Because of the above-mentioned sensitivity of data in the healthcare industry, it is regulated very heavily. Even though the main US protective data regulation HIPAA was enacted in the 90s, many institutions still have trouble complying with it. On top of HIPAA, there are many other rules on data management and storage in the US and the EU that are even trickier to follow. 

Organizations that have established data governance workflows lower the risk of non-compliance with regulations, for which they can be fined. The framework requires medical software used to handle healthcare data to be compliant with regulations by design.. What is more, data governance specialists are expected to stay informed about the current regulatory initiatives and update the organization’s strategy accordingly.

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Data governance: 3 major components

As you can see, healthcare organizations that handle loads of data on a daily basis will benefit in many ways from implementing a data governance strategy. However, a good strategy is not easy to build because it requires a synergy of three components.

1. Technical components

Technical components are the first frontier enabling the generation, sharing and use of quality data. Therefore, it is impossible to build a working data governance strategy without the following:

  1. Adherence of the hospital software infrastructure to the global open, interoperable, and pragmatic standards of storing and transferring healthcare data.
  2. Implementation of the modern data security and privacy-enhancing technologies.
  3. Constantly updated tools for data visualization and sharing.
  4. Correctly written and shared metadata that complies with the global standards.

2. Structural components

Structural components facilitate oversight and guidance of the data governance activities. This means creating a leadership team that will embed robust data governance and collaboration workflows across the company. Organization-wide actions that should be taken by the members of this team include:

  • Aligning the corporate data governance strategy with the global and local data regulations.
  • Establishing trust in the strategy and steps required for its effective execution.
  • Making sure the strategy adheres to the ethical norms of the healthcare industry and the particular region.
  • Communicating data governance requirements to employees and patients and ensuring transparency of data governance processes and objectives.

3. Legal components

Legal components represent accountability mechanisms. While structural components set clear guidelines, regulate how data is collected, processed, used, and disposed of, and inform all participants about the data guidance rules and best practices, legal components force adherence to them.

Data governance best practices

How to build a successful data governance strategy in healthcare

Having all of the components in place is half of the way to a successful data governance strategy. The rest depends on the proper execution of the implementation strategy.

Step 1. Outline the business goals and set priorities

A data governance strategy shouldn’t be self-indulgent but it also doesn’t exist in a vacuum. Instead, it must be planned according to the needs of the company as a whole, taking into account different departments’ needs and specifics. Depending on the particular healthcare company’s specialization (a hospital, a private practitioner, an insurance agent, a pharmacy, etc.), its objectives may vary a lot. At this stage, data governance specialists should work closely with the business development team in order to correctly determine the priorities and use cases for the data governance strategy based on your business goals. 

Data governance capabilities

Step 2. Understand data types and domains

After determining what exactly you’re planning to achieve, you can clearly see which data needs to be governed and what sources it comes from. 

For example, if your goal is to engage with patients more productively, data that requires the most attention would be: 

  • patients’ demographic information
  • lab and test results
  • clinical summaries of patients’ visits
  • treatment plans
  • patient feedback
  • records of the conversations with the contact center specialists
  • chatbot scenarios and outcomes

In short, you will need to govern all data related to the interactions between the patient and the organization. It usually comes from patient portals, EHRs, CRMs, wearables, phone conversations, etc, so your data governance strategy should encompass these sources as well.

Step 3. Assign roles and responsibilities

Now that you know which data your strategy is focusing on, it’s time to create a core team of data governance professionals who understand the context of this data. For instance, if you’re focusing on data related to cardiovascular disease, it wouldn’t be a good idea to engage specialists who primarily work in plastic surgery. The best team is a self-organized one with members who are able to set up processes on their own and give strategic advice to decision-makers.

The hierarchy of data governance roles

Step 4. Establish standards and policies

Practice shows that professionals who work with data daily can provide valuable insights into most suitable data governance measures, helping create a company-wide strategy. Collaboration among data specialists from departments involved is crucial from the beginning of the strategy creation to its final implementation. Standards and policies should be worked out based on the organization’s objectives, data governance components described above, and specialists’ feedback.

Step 5. Monitor the results

After implementing the strategy and setting up all the necessary processes, make sure to keep an eye on how these changes impact your company's performance. The best way to do it is to track metrics that reflect changes the data governance strategy lead to:

  • improved data quality score
  • fewer risk events
  • improved KPIs you have set in the beginning (for example, patient engagement)

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Overcoming challenges of data governance in healthcare

Implementing a data governance strategy comes with certain struggles along the way. Most healthcare companies experienced similar difficulties during the process, so it’s best to explore them and find the solution beforehand.

Challenges Success factors
Resistance to change

Remember the last time you tried to start doing yoga in the morning? Didn’t go so smoothly, right? Changing habits is hard for an individual, let alone an entire organization.

Change should inspire

Employees will be more eager to invest their time and effort into following new guidelines and getting acquainted with additional tools if they clearly understand their value for both the organization and themselves.

Miscommunication across departments

Clinicians, accountants, sales personnel, procurement specialists, IT experts, and business development professionals speak different languages and don’t always understand each other’s needs.

Create a multi-expertise leadership team

Ideally, the leadership team should include specialists from various areas that could further educate their colleagues about data governance. Additionally, by collaborating on data governance guidelines, this diverse team can implement them in their respective departments.

The complexity of the healthcare data

The amount of unstructured healthcare data is growing exponentially: clinical notes, information from EHR, test results and medical images, data from medical devices, and mobile health apps, which makes it harder to organize and govern.

Big data approach
If your healthcare company collects large volumes of data that have to be processed in short periods of time, it’s best to adopt tools and strategies developed specifically for big data governance and management.
Specialists already have their hands full

People who should be at the forefront of data governance implementation are your top-tier specialists and are usually very busy.

Clearly define your purpose and priorities

Make sure that every initiative requiring the attention of a highly skilled professional brings tangible value to a company. Develop a responsibility matrix that enables high-profile specialists to delegate data governance tasks that can be done by others.

 

In conclusion: enforcing good data hygiene 

Data governance is a complex subject, involving a variety of tools and processes. However, its significance and value are not always obvious to all employees, making it difficult for companies to not only implement data governance but also motivate employees to adhere to its rules. What is more, it is not fully established yet, since its best practices and regulations are still being developed within the healthcare industry. 

However, having a good data governance strategy in your company can be compared to washing hands during the pandemic. Following rules and sticking to strictly defined workflows might not be fun and people might not notice the result right away, but it’s crucial for the well-being of the entire organization. So just like in the case of personal hygiene, a company leader must first educate personnel about data governance, outlining its value for the company and each individual department, and only then proceed with implementing it.