What is Data Quality Management in Healthcare?

Healthcare data quality management includes technologies and statistical solutions to verify the reliability of acquired clinical intelligence. A data quality manager protects databases from digital corruption, cyberattacks, and inappropriate handling. So, medical professionals can get more realistic insights using data analytics solutions.

Laboratories have started emailing the test results to help doctors, patients, and their family members make important decisions without wasting time. Also, assistive technologies merge the benefits of the Internet of Things (IoT) and artificial intelligence (AI) to enhance living standards.

However, poor data quality threatens the usefulness of healthcare data management solutions.

For example, pharmaceutical companies and authorities must apply solutions that remove mathematical outliers to perform high-precision data analytics for clinical drug trials. Otherwise, harmful medicines will reach the pharmacist’s shelf, endangering many people.

How to Ensure Data Quality in the Healthcare Industry?

Data quality frameworks utilize different strategies to prevent processing issues or losing sensitive intelligence. If you want to develop such frameworks to improve medical intelligence and reporting, the following 7 methods can aid you in this endeavor.

Method #1| Use Data Profiling

A data profiling method involves estimating the relationship between the different records in a database to find gaps and devise a cleansing strategy. Data cleansing in healthcare data management solutions has the following objectives.

1. Determine whether the lab reports and prescriptions match the correct patient identifiers.

1. If inconsistent profile matching has occurred, fix it by contacting doctors and patients.

1. Analyze the data structures and authorization levels to evaluate how each employee is accountable for specific patient recovery outcomes.

1. Create a data governance framework to enforce access and data modification rights strictly.

1. Identify recurring data cleaning and preparation challenges.

1. Brainstorm ideas to minimize data collection issues that increase your data cleaning efforts.

1. Ensure consistency in report formatting and recovery measurement techniques to improve data quality in healthcare.

Data cleaning and profiling allow you to eliminate unnecessary and inaccurate entries from patient databases. Therefore, healthcare research institutes and commercial life science businesses can reduce processing errors when using data analytics solutions.

Method #2| Replace Empty Values

What is a null value? Null values mean the database has no data corresponding to a field in a record. Moreover, these missing values can skew the results obtained by data management solutions used in the healthcare industry.

Consider that a patient left a form field empty. If all the care and life science businesses use online data collection surveys, they can warn the patients about the empty values. This approach relies on the "prevention is better than cure" principle.

Still, many institutions, ranging from multispecialty hospitals to clinical device producers, record data offline. Later, the data entry officers transform the filled papers using scanners and OCR (optical character recognition).

Empty fields also appear in the database management system (DBMS), so the healthcare facilities must contact the patients or reporting doctors to retrieve the missing information. They use newly acquired data to replace the null values, making the analytics solutions operate seamlessly.

Method #3| Refresh Old Records

Your physical and psychological attributes change with age, environment, lifestyle, and family circumstances. So, what was true for an individual a few years ago is less likely to be relevant today. While preserving historical patient databases is vital, hospitals and pharma businesses must periodically update obsolete medical reports.

Each healthcare business maintains a professional network of consulting physicians, laboratories, chemists, dietitians, and counselors. These connections enable the treatment providers to strategically conduct regular tests to check how patients’ bodily functions change throughout the recovery.

Therefore, updating old records in a patient’s medical history becomes possible. Other variables like switching jobs or traveling habits also impact an individual’s metabolism and susceptibility to illnesses. So, you must also ask the patients to share the latest data on their changed lifestyles. 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. Furthermore, standardized reports are integral to improving data governance compliance in the healthcare industry.

Consider the following principles when promoting a documentation protocol to make all reports more consistent and easily traceable.

1. A brand’s visual identities, like logos and colors, must not interfere with clinical data presentation.

1. Observed readings must go in the designated fields.

1. Both the offline and online document formats must be identical.

1. Stakeholders must permanently preserve an archived copy of patient databases with version control as they edit and delete values from the records.

1. All medical reports must arrange the data and insights to prevent ambiguity and misinterpretation.

Pharma companies, clinics, and FDA (food and drug administration) benefit from reporting standards. After all, corresponding protocols encourage responsible attitudes that help data analytics solutions avoid processing problems.

Method #5| Merge Duplicate Report Instances

A report instance is like a screenshot that helps you save the output of visualization tools related to a business query at a specified time interval. However, duplicate reporting instances are a significant quality assurance challenge in healthcare data management solutions.

For example, more than two nurses and one doctor will interact with the same patients. Besides, patients might consult different doctors and get two or more treatments for distinct illnesses. Such situations result in multiple versions of a patient’s clinical history.

Data analytics solutions can process the data collected by different healthcare facilities to solve the issue of duplicate report instances in the patients’ databases. They facilitate merging overlapping records and matching each patient with a universally valid clinical history profile.

Such a strategy also assists clinicians in monitoring how other healthcare professionals prescribe medicine to a patient. Therefore, they can prevent double dosage complications arising from a patient consuming similar medicines while undergoing more than one treatment regime.

Method #6| Audit the DBMS and Reporting Modules

Chemical laboratories revise their reporting practices when newly purchased testing equipment offers additional features. Likewise, DBMS solutions optimized for healthcare data management must receive regular updates.

Auditing the present status of reporting practices will give you insights into efficient and inefficient activities. Remember, there is always a better way to collect and record data. Monitor the trends in database technologies to ensure continuous enhancements in healthcare data quality.

Simultaneously, you want to assess the stability of the IT systems because unreliable infrastructure can adversely affect the decision-making associated with patient diagnosis. You can start by asking the following questions.

Questions to Ask When Assessing Data Quality in Healthcare Analytics Solutions

1. Can all doctors, nurses, agents, insurance representatives, patients, and each patient’s family members access the required data without problems?

1. How often do the servers and internet connectivity stop functioning correctly?

1. Are there sufficient backup tools to restore the system if something goes wrong?

1. Do hospitals, research facilities, and pharmaceutical companies employ end-to-end encryption (E2EE) across all electronic communications?

1. Are there new technologies facilitating accelerated report creation?

1. Will the patient databases be vulnerable to cyberattacks and manipulation?

1. Are the clinical history records sufficient for a robust diagnosis?

1. Can the patients collect the documents required to claim healthcare insurance benefits without encountering uncomfortable experiences?

1. Is the presently implemented authorization framework sufficient to ensure data governance in healthcare?

1. Has the FDA approved any of your prescribed medications?

Method #7| Conduct Skill Development Sessions for the Employees 

Healthcare data management solutions rely on advanced technologies, and some employees need more guidance to use them effectively. Pharma companies are aware of this as well, because maintaining and modifying the chemical reactions involved in drug manufacturing will necessitate specialized knowledge.

Different training programs can assist the nursing staff and healthcare practitioners in developing the skills necessary to handle advanced data analytics solutions. Moreover, some consulting firms might offer simplified educational initiatives to help hospitals and nursing homes increase the skill levels of employees.

Cooperation between employees, leadership, and public authorities is indispensable to ensure data quality in the healthcare and life science industries. Otherwise, a lack of coordination hinders the modernization trends in the respective sectors.

Conclusion

Healthcare analytics depends on many techniques to improve data quality. For example, cleaning datasets to eliminate obsolete records, null values, or duplicate report instances remains essential, and multispecialty hospitals agree with this concept.

Therefore, medical professionals invest heavily in standardized documents and employee education to enhance data governance. Also, you want to prevent cyberattacks and data corruption. Consider consulting reputable firms to audit your data operations and make clinical trials more reliable.

SG Analytics is a leader in healthcare data management solutions, delivering scalable insight discovery capabilities for adverse event monitoring and medical intelligence. Contact us today if you want healthcare market research and patent tracking assistance.

Data Analyst providing the insights of all types of industries such as ESG, Healthcare, Market Research and so on