Healthcare data quality management involves utilizing advanced technologies and statistical solutions to verify the reliability of acquired clinical intelligence. A data quality manager is responsible for safeguarding databases from digital corruption, cyberattacks, and inappropriate handling, enabling medical professionals to obtain more realistic insights through data analytics solutions.

One significant advancement in healthcare is laboratories emailing test results to help doctors, patients, and their families make timely decisions. Additionally, assistive technologies are merging the benefits of the Internet of Things (IoT) and artificial intelligence (AI) to enhance living standards. However, poor data quality poses a significant threat to the usefulness of healthcare data management solutions. For instance, pharmaceutical companies and regulatory authorities must apply solutions to eliminate mathematical outliers and ensure high-precision data analytics for clinical drug trials. Failure to do so could result in harmful medicines reaching pharmacy shelves, endangering lives.

How to Ensure Data Quality in the Healthcare Industry

Data quality frameworks utilize various strategies to prevent processing issues and the loss of sensitive information. Here are seven methods to develop such frameworks and improve medical intelligence and reporting.

Method 1: Use Data Profiling

Data profiling involves estimating the relationships between different records in a database to identify gaps and devise a cleansing strategy. In healthcare data management, data cleansing has several objectives:

• Determine whether lab reports and prescriptions match the correct patient identifiers.
• Fix inconsistent profile matching by contacting doctors and patients.
• Analyze data structures and authorization levels to evaluate employee accountability for patient recovery outcomes.
• Create a data governance framework to enforce strict access and data modification rights.
• Identify recurring data cleaning and preparation challenges.
• Brainstorm ideas to minimize data collection issues that increase data cleaning efforts.
• Ensure consistency in report formatting and recovery measurement techniques.

Data cleaning and profiling help eliminate unnecessary and inaccurate entries from patient databases, reducing processing errors when using data analytics solutions.

Method 2: Replace Empty Values

Null values, or missing data, can skew the results obtained by healthcare data management solutions. For example, if a patient leaves a form field empty, online data collection surveys can warn patients about the empty values, applying the "prevention is better than cure" principle. However, many institutions still record data offline, transforming filled papers using scanners and optical character recognition (OCR). Healthcare facilities must contact patients or reporting doctors to retrieve and replace missing information, ensuring seamless operation of analytics solutions.

Method 3: Refresh Old Records

Physical and psychological attributes change with age, environment, lifestyle, and family circumstances. Therefore, what was true for an individual a few years ago may not be relevant today. While preserving historical patient databases is essential, hospitals and pharmaceutical businesses must periodically update obsolete medical reports. Professional networks of consulting physicians, laboratories, chemists, dietitians, and counselors enable treatment providers to conduct regular tests and update patients' medical histories. Freshly obtained records increase the relevance of healthcare data management solutions.

Method 4: Standardize Documentation

Standardization compels all professionals to collect, store, visualize, and communicate data or analytics activities using unified reporting solutions. Standardized reports are integral to improving data governance compliance in the healthcare industry. Key principles to promote a documentation protocol include:

• Ensuring brand visual identities, like logos and colors, do not interfere with clinical data presentation.
• Placing observed readings in designated fields.
• Maintaining identical offline and online document formats.
• Preserving an archived copy of patient databases with version control.
• Arranging data and insights to prevent ambiguity and misinterpretation.

Pharmaceutical companies, clinics, and the FDA benefit from reporting standards, encouraging responsible attitudes that help data analytics solutions avoid processing problems.

Method 5: Merge Duplicate Report Instances

A report instance is like a screenshot that saves the output of visualization tools related to a business query at a specified time interval. Duplicate reporting instances are a significant quality assurance challenge in healthcare data management. For example, multiple nurses and doctors interacting with the same patients can result in multiple versions of a patient’s clinical history. Data analytics solutions can process data collected by different healthcare facilities, merging overlapping records and matching each patient with a universally valid clinical history profile. This strategy also helps clinicians monitor other healthcare professionals' prescriptions, preventing double dosage complications.

Method 6: Audit the DBMS and Reporting Modules

Chemical laboratories revise their reporting practices when new testing equipment offers additional features. Similarly, DBMS solutions optimized for healthcare data management must receive regular updates. Auditing current reporting practices provides insights into efficient and inefficient activities, ensuring continuous enhancements in healthcare data quality. Key questions to assess data quality in healthcare analytics solutions include:

• Can all stakeholders access the required data without problems?
• How often do servers and internet connectivity stop functioning correctly?
• Are there sufficient backup tools to restore the system if something goes wrong?
• Do healthcare facilities employ end-to-end encryption (E2EE) across all electronic communications?
• Are new technologies facilitating accelerated report creation?
• Are patient databases vulnerable to cyberattacks and manipulation?
• Are clinical history records sufficient for a robust diagnosis?
• Can patients collect the documents required to claim healthcare insurance benefits without discomfort?
• Is the implemented authorization framework sufficient for healthcare data governance?

Method 7: Conduct Skill Development Sessions for Employees

Healthcare data management solutions rely on advanced technologies, requiring specialized knowledge. Pharma companies recognize this, as maintaining and modifying chemical reactions involved in drug manufacturing necessitate expertise. Training programs can help nursing staff and healthcare practitioners develop the skills needed to handle advanced data analytics solutions. Cooperation between employees, leadership, and public authorities is indispensable to ensure data quality in healthcare and life sciences, supporting modernization trends in these sectors.

Conclusion

Healthcare analytics depends on various techniques to improve data quality, such as cleaning datasets to eliminate obsolete records, null values, and duplicate report instances. Standardized documentation and employee education enhance data governance, while preventing cyberattacks and data corruption. Consulting reputable firms to audit data operations ensures reliable clinical trials and better healthcare outcomes. SG Analytics is a leader in healthcare data management solutions, offering scalable insight discovery capabilities for adverse event monitoring and medical intelligence. Contact us today for healthcare market research and patent tracking assistance.