Healthcare organisations are, more than ever, relying on the utilisation of big data for healthcare to better their decision-making process. However, poor data quality will impede accurate analyses, and any decision made based on such data will be flawed. What does poor data quality do to healthcare? Poor-quality healthcare data can lead to revenue losses and, quite possibly, even loss of life for patients. 

Often, one erroneous piece of information in a provider’s EHR system causes wrong treatment, thereby adversely affecting patient outcomes. Besides, patient data is very sensitive and calls for very strict regulatory and security measures. Hence, the need for the well-established management of healthcare data quality. More and more, synthetic healthcare data is being employed for model training and analytics to comply with privacy regulations. Under regulations such as HIPAA, the establishment of data quality protocols makes certain that providers can protect the highly sensitive patient information and still benefit from data analytics in healthcare.

Why Is Data Quality Important In Healthcare?

Trust is one of the most significant things in this industry. As indicated, accuracy, completeness, and consistency of data are critical for acceptable diagnosis and treatment of patients. But compliance, that is, establishing how data should conform to the quality requirements, is an equally important guarantee of healthcare data quality. 

There are several regulatory compliance standards, like HIPAA rules, that help ensure the data goes in for safe transmission. In a global arena, healthcare providers are none other than organisations that need to stay with their competitors and harvest as much from their opportunities.

Data quality will be beneficial for the health sector in various ways:

1. Informed Decision-Making

The more accurate information an organisation can access, the better it can make and stand by its decisions. Data quality in healthcare minimises risk, enhances care outcomes, and ultimately increases patient satisfaction. 

2. Better Patient Targeting

Healthcare data accuracy tells marketers what they should be targeting instead of broadly. Sometimes, organisations are left guessing who that target really is because of faulty data. Know which kind of potential patients could be sourced from your current patients. Quality data from existing persons will help in the identification of similar potential customers and enable fine-tuning of marketing campaigns and healthcare procedures.

3. Strengthening Physician-Patient Relations

Good relationships with customers, in your case, patients, are the success mantra for a healthcare organisation. The best way to get to know the audience is to collect information from them and make available the facilities they demand. This goes a long way in strengthening the bond between a patient and the carer.

Some practitioners, in the other direction, have used telemedicine as a means to further strengthen their relations with patients. They protect patients while giving the physician some knowledge on the situation. Telemedicine software vendors are the ones famed for fast upholding cutting-edge innovations in patient relations.

4. Easier Data Implementation

High-quality data are very suitable for use, and therefore they add significantly to organisational efficiency. Time is money and a costly resource, which should rather be spent on fruitful enterprises than mending inconsistent or incomplete data to make them usable. In essence, accuracy of the data provides the healthcare organisation with less implementation time for any actionable insight obtained from data analysis. 

5. Profitability 

Better patient relations, better decision-making, and better marketing will translate into bigger profits. Data quality in the healthcare sector promotes efficiency and saves on costs; therefore, operations run smoothly, resulting in higher profits. 

Key Components of Data Quality Management

Data quality focused on the healthcare sector is important for serious decision-making. Using various techniques can make the data more trustworthy for improving patient care. The key components of data quality management are:

1. Data Governance 

A data governance framework is also a prerequisite for data quality management in healthcare. It entails the overall policies, procedures, and responsibilities exercised to manage and protect data in its life cycle. These responsibilities include defining data ownership, appointing data stewardship tasks, and establishing data quality standards. Through this governance arrangement, healthcare organisations can ensure accountability and transparency in their data management approaches.

2. Data Standardisation 

The process of standardising how healthcare information is presented, coded, and classified refers to data standardisation. Standardisation enables data to be shared and compared across various healthcare contexts. Established standards that facilitate this include HL7 and ICD/CP coding.

The reality is that the implementation of data standardisation is hindered by the fact that legacy systems are numerous, with each organisation having its own interpretation of the standards. Best practices for successful data standardisation include networking for collaboration by all the stakeholders, proper documentation of the standards, and gradual transition of the systems into standardisation. In the end, a successful data standardisation programme has been realised to improve the patient experience and processes run more smoothly, in addition to strong research initiatives. By offering various data management courses at London’s premier hub of training and consulting, healthcare organisations can also elevate their data management practices.

3. Data Validation and Verification

Data validation and verification refer to reliable and consistent mechanisms that a healthcare institution implements in the accuracy of healthcare data. Different validation procedures verify data integrity checks, among other statistical validation procedures, such as cross-checking the information against trusted sources. Also, outlier analysis, consistency checks, and thorough data profiling aid the identification and resolution of errors in healthcare data. 

Integrating automation tools, such as machine learning algorithms and software for data validation and automated processing of data in health care, further enhances efficiencies, reducing the need for manual intervention and supporting provisions for real-time validation. These comprehensive approaches enhance the integrity of health data, promote informed decision-making, and uplift patient care quality while creating a more effective health system. 

4. Monitoring and Reporting Data Quality

Data quality monitoring and reporting serve as cornerstones to uphold the integrity of healthcare data. Continuous monitoring would involve a periodic appraisal of the quality performance measurement in terms of maintenance of accuracy, completeness, and consistency.

Properly defined KPIs for data quality performance would be established from these measures, which would aid healthcare institutes to evaluate themselves continuously and, in some instances, target improvement.

Providing robust reporting mechanisms with a user-friendly dashboard would lead to monitoring of data quality trends with respect to application systems. Such tools would empower the relevant stakeholders to discover anomalies promptly, address emerging issues, and make informed decisions. 

Continuous monitoring, KPIs, and reporting on a holistic system help healthcare organisations sustain and advance data quality in a proactive manner – with the eventual outcome of improved patient care – with evidence backing decisions. It would continue to add to those efforts by providing health organisations with specialised tools and knowledge for managing, organising, and optimising their healthcare data.

Healthcare Data Quality Standards

Healthcare data quality standards are all about HL7 integration, which provides a route for exchanging and storing data, aligning with the benefits of integrated healthcare systems. HL7 standards not only set regulations on how data is transmitted but also specify the structure of data, language, and data type. With HL7 providing support for practices, management, and insights, it ensures a seamless integration between two systems, facilitating efficient and effective healthcare data exchange. HL7 ensures interoperability between systems, specifically health information systems, to assure consistency in documentation across all systems regardless of their organisation.

HL7 standards include the following:

1. Structured project labelling
2. Messaging standards
3. Clinical document structures
4. Care document continuation