Data quality is subjective for effective healthcare data analytics. It affects the accuracy, reliability, and validity of the information derived from the data. Poor data quality can lead to incorrect diagnoses, ineffective treatments, and increased risks for patients and providers. Therefore, identifying and resolving critical data quality issues are fundamental for healthcare managers looking to leverage data analytics for enhancing healthcare outcomes and performance.
Quality Data is the Key
The first step to identifying critical data quality issues is to determine what data quality implies for specific contexts and goals. Data quality can be assessed depending on dimensions like accuracy, completeness, consistency, relevance, and integrity. Depending on the type and purpose of the data analysis, some dimensions can be more important than others.
The growing number of innovations in healthcare are enabling doctors to systematically provide better care to patients. While doctors learn from the experience of other doctors, we, as patients, are aware that healthcare is complex and not always efficient. Individual doctors learn from treating their patients, but rarely that information is further used to improve the care delivered by other doctors.
But if healthcare is not using routine care to learn, what data are doctors utilizing to make critical decisions?
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Primarily, healthcare uses a defined approach. Randomized trials are set up over a span of several years; results are analyzed and used to work their way into clinical practice slowly. While it is possible to identify whether a treatment is safe and effective, there isn’t enough information to compare treatment options and discover which treatment works best.
In short, while the captured information in such trials is good, it is not sufficient. There is not enough data for tailored therapy or rapid learning within healthcare.
Data Quality in Healthcare
Data quality in healthcare helps in defining the cost paid for medical services. With the growing rise in the adoption of artificial intelligence (AI), data analytics, the Internet of Medical Things (IoMT), and data visualization tools, the significance of data quality in healthcare cannot be understated.
In the healthcare industry, data quality indicates the data collected by a medical organization that meets the following characteristics:
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Accuracy: Data is considered accurate only when every detail entry of the information is correct and properly presented.
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Completeness: Completeness implies that all information collected by the provider is documented and easily accessible.
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Relevance: The relevance factor is met when the data is collected to be used in the medical setting as well as for medical purposes.
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Legality: This indicates that the data collection, processing, storage, and use process complies with all legal requirements and standards.
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Consistency: Data can be deemed consistent only when it is constantly updated and reflects the current state of the patient’s health as well as medical interventions.
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Accessibility: The accessibility criterion is met when medical staff have full access to the details they require and can use to undertake their responsibilities.
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In a nutshell, the quality of data accumulated from various solutions is likely to impact decision-making processes on individual as well as global levels. If the collected data lacks any of the above-mentioned attributes or if the data quality is poor, it means that the use of such faulty data can lead to negative consequences for patients, hospitals, and researchers.
Technology and Innovation in Healthcare
Healthcare as an industry is starting to learn from real-world care. While the infrastructure has always been in place, the recent confluence of data - electronic health records, technology such as artificial intelligence, and computing power has led to the creation of an environment where a learning health system is possible and anticipated.
What healthcare learns from everyday care can be transformed into data. These learnings further help in supporting a better understanding of the unique characteristics of every human being. It aids in the recognition of how unique characteristics influence the effectiveness of available treatment options, along with offering tailored care to the individual.
The speed of adoption of IT solutions in healthcare is incredibly fast. This has led to the creation of plenty of trends that are constantly changing and creating continuous progress as well as improvement. However, these tendencies are likely to affect data quality management in the near future.
However, learning the wrong lesson from bad data is not just a problem but a serious issue of concern. The industry makes decisions based on these recommendations. This can cause serious damage to patients, and their confidence in the validity of evidence can be shaken.
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Here, the lesson is clear: If the healthcare sector is going to learn from routine care, they must protect patients by ensuring sufficiently high data quality to explain the recommendations.
New IT solutions are assisting with the collection and processing of high-quality healthcare data that has led to critical advancements in healthcare data management. Aligning the insights with its mandate will help protect the patients. In the process, they can define the data-quality standard sufficient for their use along with real-world evidence. This standard can encourage key decision-makers, including doctors, insurance firms, and regulators, to decide whether real-world evidence is sufficiently credible to influence the standard procedures in healthcare.
Operating with quality data develops the ability of healthcare providers to predict results and avoid situations that may lead to poor patient outcomes. It also helps in improving hospital management and staff administration. The quality of data standards will further help in measuring accuracy, completeness, and traceability.
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In Conclusion
Today, relatively few treatment decisions are guided by real-world evidence in a learning healthcare system. Every treatment decision is influenced by previous practice. Without a critical emphasis on accuracy, completeness, and traceability, there will likely be a significant risk. Not all firms generating healthcare evidence employ high-quality data or measure data quality. And relying on evidence-based on poor-quality data can prove catastrophic.
But there is hope for a bright future for healthcare.
Healthcare institutions are employing modern technologies to learn from the most robust healthcare data. But, in such scenarios, data quality must be paramount.
This transition to a learning health system has become more critical than ever for the healthcare industry. The availability of electronic health data, computing power, and artificial intelligence (AI) will be transformative. However, it is equally vital for healthcare industry professionals to learn to distinguish high-quality data from low-quality data and ensure they learn the right lessons.
The payoff will be better, safer, and more efficient care for us all.
SG Analytics, recognized by the Financial Times as one of APAC's fastest-growing firms, is a prominent insights and analytics company specializing in data-centric research and contextual analytics. Operating globally across the US, UK, Poland, Switzerland, and India, we expertly guide data from inception to transform it into invaluable insights using our knowledge-driven ecosystem, results-focused solutions, and advanced technology platform. Our distinguished clientele, including Fortune 500 giants, attests to our mastery of harnessing data with purpose, merging content and context to overcome business challenges. With our Brand Promise of "Life's Possible," we consistently deliver enduring value, ensuring the utmost client delight.
A leader in the Healthcare domain, SG Analytics is assisting healthcare companies to leverage the power of information. Contact us today if you are in search of efficient Healthcare solutions to make sound business decisions.
About SG Analytics
SG Analytics is an industry-leading global insights and analytics firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company and has a team of over 1100 employees and has presence across the U.S.A, the U.K., Switzerland, Canada, and India.
Apart from being recognized by reputed firms such as Analytics India Magazine, Everest Group, and ISG, SG Analytics has been recently awarded as the top ESG consultancy of the year 2022 and Idea Awards 2023 by Entrepreneur India in the “Best Use of Data” category.
