Healthcare data governance: components, benefits & strategy building

Healthcare data governance: components, benefits & strategy building

January 4, 2024

Sergey Ivanov

by Sergey Ivanov,

Head of Healthcare Center of Excellence

Data governance is a set of practices that ensures safe and effective usage of patient records, test results, medical images, prescriptions, and other sensitive medical data, which should be protected at each stage of its lifecycle and processed correctly to aid clinical decision-making.

Itransition helps healthcare organizations to establish a data governance strategy while tapping into healthcare software development services to ensure data quality and safety and make the most out of the data analytics solutions.

Table of contents

Market statistics

Data
lifecycleStoreCaptureProcessUseDisposeMaintaining&archivingAccess,sharing&analysisDestructionperretentionscheduleRecordingdatainhealthinformationsystemsSeriesofactionstakentocreateaproductand/orservice

Scheme title: Data lifecycle by AHIMA
Data source: ahima.org — Healthcare Data Governance, 2022

62%

of US patients are concerned about the safety of their information

The Pew Charitable Trusts, 2020

$11 mn

the cost of an average data breach in healthcare

IBM, 2023

175 zb

of health data will be produced in 2025

IDC, 2022

Data governance components

Healthcare organizations handle loads of health information and other data daily through many stages of its lifecycle. The best way to eliminate chaos in data management is a solid data governance strategy. However, a good strategy is challenging to build because it requires a synergy of three components, all of which concern people, processes, and technology.

Technical components

The first frontier enabling the generation, exchange, and use of quality data are technical components that include:

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

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 entire organization:

  • Aligning a corporate data governance strategy with global and local data governance policies
  • Establishing trust in the strategy and steps required for its effective execution
  • Making sure the strategy adheres to 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

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.

Technical

Structural

Legal

Open standards & interoperabilityMetadata, provenance & attributionData security & privacy enhancing technologiesData hubs, portals & visualisation/dissemination toolsData trusts, data intermediaries & data institutionsGlobal principles & normsEthics
& data governance committeesPublic-patient participation processesData sharing contractsData audit, certification & assuranceGlobal IP & data governance frameworksOpen data licensing

Scheme title: Data governance components & best practices


Data source: cdn.who.int — Health data governance summit

How to build a data governance strategy

Having all components in place is only part of a successful data governance strategy. The rest depends on the proper execution of the implementation plan.

1

Outline business goals & set priorities

A data governance strategy must be planned according to the needs of the company as a whole and different departments’ specifics. Depending on a particular healthcare company’s specialization (a hospital, a private practitioner, a health insurance agent, or a pharmacy), its objectives can significantly vary. At this stage, data governance specialists should work closely with the business development team to correctly determine the priorities and use cases for the data governance strategy based on certain business goals.

2

Understand data types & 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 assets that require the most attention would be: patients’ demographic information, medical records, treatment plans and patient outcomes, patient feedback, records of conversations with contact center specialists, and chatbot scenarios.

3

Assign roles & 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, you can engage clinicians with proven expertise in the cardiology field and adjacent areas. The best team consists of self-organized members capable of setting up processes and giving strategic advice to stakeholders.

4

Establish standards & policies

Practice shows that professionals who work with data daily can provide valuable insights into the most suitable data governance frameworks. Collaboration among data specialists from all the involved departments is crucial from strategy creation to its final implementation. Standards and policies should be worked out based on the organization’s objectives, data governance components, and specialists’ feedback.

5

Monitor the results

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

  • improved data quality score
  • fewer risk events
  • improved KPIs (for example, patient engagement)

Software for data governance

Data governance is not an abstract concept but only exists as long as there are assets to govern, which come from various sources, including the most common healthcare software. If you use one or more health IT systems, your data governance strategy should cover them.

EHR

EHR

Electronic health record solutions are one of the main patient data sources in the industry (personal and medical histories, treatment plans, adverse reactions records, etc.), and should be secure, reliable, and interoperable to support an effective data governance strategy.

Hospital management software

HMS solutions contain terabytes of information about hospital equipment, personnel scheduling, and resource distribution between departments that should fall under the health data governance regulations.

Laboratory information systems

Patients’ test results, clinical research protocols, and laboratory supply chain documentation are just a few examples of data in LIS solutions which must be included in healthcare data governance strategy.

Pharmacy management software

Data about medication origins, expiration dates, and possible adverse effects, as well as personal details about pharmacy customers, their orders, payment methods, and other sensitive information contained in the PMS should be governed.

Healthcare data analytics software

Analytics tools process the internal data of healthcare organizations and external data from various sources to gain valuable insights. Data governance policy regulates data acquisition, processing, representation, transferring, and disposal after the analytical task is complete.
Medical device software

Medical device software

Medical devices and IoMT gadgets receive sensitive data every day, including information they record themselves (like patient vitals or temperature in the ward) or that is input by patients or caregivers (e.g., symptoms). All this data is part of the data governance strategy.

Our services

Crafting a successful data governance strategy and enacting it in everyday operations requires collaboration between parties handling data, decision-makers, and solutions’ vendors. It also often involves changes in the organization's processes, software, and hardware infrastructure. Itransition’s experts with comprehensive industry experience can help healthcare providers at each stage of this complex process.

Consulting

Our consultants leverage our Healthcare Center of Excellence knowledge to properly assess your existing data sources and strategy and recommend the optimal way to transform it according to your business objectives.

Implementation

We can develop healthcare software from scratch adhering to your organization’s data governance requirements from step one, customize existing platforms so they fit your strategy, or integrate third-party tools into your healthcare IT ecosystem.

Migration

Our specialists will facilitate secure and compliant data migration for your healthcare organization within the established data governance framework.

Support

We offer support services, including post-implementation analysis of how a new data governance strategy influences your organization's processes and profits, further strategy optimization, and personnel training.

Need an effective data governance strategy for your healthcare software?

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Benefits of healthcare data governance

Health data stands out from other data types in many ways, but the most important one is that its quality, security, and accessibility to clinicians largely determine patient outcomes. Ensuring data quality is often the ultimate goal of an organization’s data governance program because it leads to a number of advantages.

1 Better clinical & business decisions

Even the most innovative healthcare BI solutions won’t show a realistic picture that decision-makers can rely on while developing patient care plans or business strategies unless it is based on quality data sets. There are multiple solutions that structure and format data from the point of entry but they still need to be tuned and overseen by professionals. To avoid the additional structuring and verification and use data right away, healthcare companies should combine employee effort and reliable software.

2 Enhanced interoperability across departments

One of the major problems in the healthcare industry is data silos. Data standardization, an integral part of the data governance framework, can solve this problem. That’s 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. It implies a set of rules on how to collect data and from what sources, which makes it suitable for sharing between departments and healthcare organizations. The improved interoperability in healthcare contributes to better patient experiences and clinicians’ higher job satisfaction.

3 Data protection

Most data in healthcare falls under either PII (personal identifiable information) or PHI (protected health information). Healthcare providers should guarantee data security or risk losing their reputation and funding. Data governance practices regulate the ways data can be stored and transferred. Its guidelines oblige healthcare organizations to use software with appropriate levels of security and include strict sensitive data handling rules for employees.

4 Improved regulatory compliance

Even though HIPAA, the main US data protection standard, was introduced in the 90s, many institutions today still require help to comply with it. Beyond HIPAA, many other rules on data management and storage in the US and the EU are even trickier to follow. Organizations with established effective data governance workflows lower the risk of non-compliance with regulatory standards. For instance, data governance specialists should stay informed about the current regulatory initiatives and update the organization’s strategy accordingly.

Common data governance challenges in healthcare

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

Challenges

Success factors

Resistance to change

Challenges

Changing habits is hard for an individual, let alone an entire organization. Clinicians that don’t see how new workflows can optimize processes and patients’ health outcomes might refuse to follow new guidelines or become dissatisfied with their job.

Challenges

Success factors

Employees will be more eager to invest their time and effort into following new guidelines and getting acquainted with additional health systems if they clearly understand the importance of data governance for the organization and their own work.

Miscommunication across departments

Challenges

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

Challenges

Success factors

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 new principles in their respective departments.

The complexity of healthcare data

Challenges

The amount of unstructured healthcare data becomes harder to organize and govern because of the growing number of sources: clinical notes, electronic health records, test results and medical images, data from medical devices, and mobile health apps.

Challenges

Success factors

Suppose your healthcare company collects large volumes of patient data to process in short periods of time. In that case, adopting tools and strategies developed specifically for big data governance and management is a viable solution.

Overbooked specialists

Challenges

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

Challenges

Success factors

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.
Boost your healthcare business with good data hygiene

Boost your healthcare business with good data hygiene

An effective data governance strategy in your company can be compared to washing hands during the pandemic. With no immediately visible effect, sticking to strictly defined data management workflows could eventually become crucial for the well-being of the entire organization. However, building and implementing a suitable and effective data governance strategy is tricky for people without experience in such a field. In such cases, our experts can provide all services necessary to achieve the desired results.

Boost your healthcare business with good data hygiene

Ready to secure and enhance your healthcare organization’s data governance?

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FAQ

What is information governance in healthcare?

The Information Governance Initiative (IGI) defines information governance as activities and technologies 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.

What is the difference between data governance & data management?

Data governance is part of a larger information governance strategy, mostly carried out by an organization’s IT department, that deals with individual pieces of digital data and their sources. It encompasses all activities to keep data reliable, structured, accessible, and protected. Data management is a combination of actions performed on data that’s regulated by the data governance principles.

What are the characteristics of quality data?

According to The American Health Information Management Association (AHIMA) 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

Is the FDA involved in healthcare data governance?

The FDA promotes data governance best practices and adheres to the data governance strategy created for this organization specifically to improve public health.

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