Access to accurate, complete, and timely data is critical in the healthcare industry. It impacts patient care as well as government budgets for the maintenance of health services.
Unfortunately, most healthcare facilities are dogged by poor data quality and large backlogs of medical records that need to be improved for it to be accessible & usable. Outdated systems, poor data culture, and the reluctance to onboarding new technologies are some of the biggest obstacles to data quality in healthcare.
The rule is simple – if healthcare authorities want to maintain and improve healthcare at an optimal level, they need to ensure adherence to data quality standards.
In this quick post, we’ll cover what data quality means for healthcare, its challenges, limitations, and the immediate steps industry leaders can take to improve the quality of data.
What Does Data Quality Mean for Healthcare?
Health data organized, aggregated and transformed into a meaningful format provides health information that can be used to:
- Optimize patient care with accurate data
- Consolidate data to get accurate patient overview
- Enable confidence in data reliability
- Create reports with reliable statistics
- Empower employees and staff to make critical decisions based on accurate data
Because the stakes are so high, it’s of utmost importance for healthcare data to be factual, organized, valid, accurate & accessible.
How is Data Quality Determined?
In healthcare, data quality refers to the users’ level of confidence in the data. This confidence is at its highest if the following standards are maintained.
Accuracy and validity: Original data source is not misleading or corrupt
Example of Accuracy and validity:
- Patient’s identification details and address are valid
- Vital signs are recorded within acceptable value parameters
- Codes used in hospitals to classify diseases and procedures conform to pre-defined standards
Reliability and consistency: Information follows a set standard throughout the organization
Examples of reliability and consistency:
- Age of patient recorded in one record is the same in all other records
- Correct name/gender/marital status is the same in all records
- Correct format of phone number/address is the same in all records
Completeness: All required data fields are present
Examples of completeness:
- Nursing notes, including nursing plan, progress notes, blood pressure, temperature, and other charts are complete with signatures and date of entry
- For all medical/health records, relevant forms are complete, with signatures and dates of attendance.
- For inpatients, the medical record contains an accurately recorded main condition and other relevant diagnoses and procedures and the attending doctor’s signature.
Currency and Timeliness: data is up to date
Examples of timeliness:
- A patient’s identifying information is recorded at the time of first attendance and is readily available to identify the patient at any given time.
- The patient’s past medical history, a history of the present illness/problem as detailed by the patient, and results of physical examination, is recorded at the first attendance at a clinic or admission to the hospital.
- Statistical reports are ready within a specified time frame, having been checked and verified.
Accessibility: Data is available to authorized persons as and when needed
Examples of Accessibility:
- Medical/health records are available when and where needed at all times.
- Abstracted data are available for review when and where needed.
- In an electronic patient record system, clinical information is readily available when needed.
Data quality in healthcare is of crucial importance not just for patient care but also for monitoring the performance of health service and employees. Data collected and presented must meet these standards. The problem? A reliance on traditional methods of data management causes hospitals and information exchanges (HIEs) to struggle with patient matching issues, poor algorithms, chaotic processes, operational inefficiency, poor data literacy, and poor data quality.
High-quality data embodies these internationally followed standards, however, current limitations in terms of technology, resources, and processes have made it a challenge for healthcare facilities to reach these objectives.
The COVID-19 is a perfect example of how challenges in data quality affect pandemic handling. Organizations that were data-driven responded swiftly with apps, predictive analytics, and patient care models that helped the world cope. Those that previously ignored digital transformations were jolted into realizing the need for adapting to ML/AI technologies (for which accurate data is the foundation).
How the COVID-19 Has Exposed the Healthcare Industry’s Data Quality Challenges and Limitations
Pandemics have always challenged the infrastructure of the healthcare industry, but the COVID-19 has added a new challenge – that of digital transformation and the need for improved, aggregated data.
Healthcare facilities are at wits’ end trying to leverage real-time data-driven insights to make critical decisions. Part of what makes this so challenging is the current outdated data infrastructure that still relies on manual methods for data entries and data aggregation. Complex data storage, coupled with disparate data sources, and a staff lacking in data training makes it difficult to collect, process, and consolidate data to provide the complete picture of a patient – the implications of which results in skewed analytics and patched data providing a far from accurate view of the pandemic.
In a brilliant article on the impact of poor data quality on COVID-19 response, Datanami reports that new case counts and hospitalization beds data is reported manually by hospitals and that creates a challenge in getting the high confidence in that current ‘heads and beds’ data.
The volume and variety of data generated during this pandemic is unimaginable. Healthcare facilities are pressed to make sense of this data fast to meet challenges head-on, but with a reliance on manual processes, a generally slow approach to technology-driven initiatives and the existing use of legacy systems has made it difficult for real-time decision making.
Fortunately, all is not doomed. The pandemic has ignited accelerated efforts to the use of tools and technologies that allow hospitals, healthcare facilities, governments, pharmaceutical companies, research organizations to aggregate and analyze a diverse multitude of data sets to produce solutions (such as mobile apps that predict risks), patient care guidelines, and the creation of vaccines in record time.
What Immediate Steps Can the Industry Take to Achieve Data Quality Goals?
Leadership, training, culture change is some of the most common advice experts give, but these steps require long-term overhaul. At a time when leaders are pressed to prioritize data quality, they need to take actionable, immediate steps. These include:
Conducting a data quality audit:
Solutions can only be derived if you know exactly the problem your organization is facing. For instance:
- Are your teams struggling to consolidate patient data from multiple resources for a report?
- Is your facility struggling with errors during data entry?
- Do you have poor data controls in place?
- What are some of the most common errors found in your records?
These and many more questions need to be asked. Records must be pulled up and evaluated to see if it meets quality standards defined.
Investing in a self-service data quality tool:
Chances are your team is still relying on ETL tools to clean and transform data. Manual methods cannot be used to process data with exponential volume and variety. This is where ML-based self-service data quality tools come in handy. They replace manual data cleansing or standardization efforts with quick, automated processes. For instance, normalizing hospital data takes months of effort and involves complex processes as ensuring the right [name] [date] [phone number] formats. With a self-service tool, it takes just minutes to capitalize names, remove white spaces, fat-finger mistakes and much more for a million rows.
Automate data preparation:
Automation is the future. For the healthcare industry, automation is a necessity that can positively impact patient care, resource management, system management, statistics, funding and so much more. Old guarded beliefs and reliance on outdated processes must be replaced with innovation and automation with the fundamental goal of enabling human resources to focus more on analysis and decision-making.
Define data quality standards:
Data must be measured to reflect the dimensions of data quality standards. To begin with, organizations must ensure their current data is accurate, complete, and valid.
Make data quality an organizational habit:
Functions of data quality such as data cleansing, data standardization should not be performed only when needed. Organizations must develop a routine to clean and keep data up-to-date. Employees with access to this data must be trained to understand data quality and the implications it has on downstream applications. This particular step does not require an organizational change – instead, it can be accomplished quite simply by creating a schedule, assigning a resource, and empowering the resource with the right tool to get the job done.
How does Data Ladder help?
Data Ladder’s DataMatch Enterprise is a best-in-class solution designed to help the healthcare industry with data quality management. With Data Ladder, your team can process terabytes of data, consolidate multiple data sources, clean and transform millions of rows of data within just 45 minutes.
See how we helped West Virginia University with data quality and record linkage in this case study.
DME is the chosen tool of choice for healthcare organizations due to its easy to use interface, 100% record linkage accuracy, and its ability to perform data transformations CODE-FREE.
DME can help healthcare systems with:
Record Linkage for Longitudinal Studies
Data linking is the process of linking/combining/bringing together multiple sources of information on one individual or entity. The combination of information has several advantages:
- Longitudinal studies for entire populations can be conducted to understand disease trends and correlated challenges.
- Implementing changes or developing new health policies in the light of available data.
- Experts can discover or resolve questions to answers which a single data set cannot provide.
- Historical information such as administrative data, vital events data etc, collected over the lifetime of a population is valuable in studying illnesses and identifying susceptible populations.
- Combining multiple datasets allows organizations to evaluate the state of their data quality at a deeper level and identify potential gaps to be filled in.
- Simulation models can be developed to study different populations
Also known as ‘record linkage,’ data linking was first proposed by Halbert L. Dunn in 1946 in his article titled, ‘Record Linkage,’ in the American Journal of Public Health, where he suggested the creation of a ‘book of life’ for each individual from birth to death, incorporating key health and social events. This book would be a compilation of all existing records to create a singular file for use in health service planning.
Ever since, healthcare facilities across the world including US, Canada, England, Denmark and Australia have endeavored to create data linkage systems. These systems hold datasets on births, deaths, hospital admissions, emergency attendances, and much more. Some countries even have extensive records on mental health, education, genealogy, and specific research data.
A wide gulf exists between the ideal data system and the current federal health care data system. Poor quality data stored in fragmented systems and the absence of quality monitoring means healthcare facilities are facing significant challenges in providing high-value healthcare.
Additionally, unprecedented expansion of patient data from sources as the internet and mobile has increased the volume and variety of data exponentially, making it difficult for organizations to link electronic health records (EHR) across and between systems – an activity necessary for a range of purposes including healthcare research, longitudinal studies of populations, disease prevention and control, patient care and much more.
Code-free Data Parsing, Cleansing & Standardization
DME allows for easy, point-and-click data cleansing. Unlike ETL tools or Excel, there is no manual effort involved. With DME, users can:
- Transform poor data by simply clicking on checkboxes.
- Normalize text style.
- Remove unwanted characters
- Remove accidental typos during data entry (these are hard to catch!)
- Clean up spaces between letters/words
- Transform nicknames into actual names (John instead of Johnny)
DME allows for easy uniformity of data by letting the user choose from over a dozen standardization options that can be applied to hundreds of millions of records at a time (tested with 2 billion+ records).
Enable the Implementation of a Data Quality Framework
DME’s platform is a framework that enables organizations to find a starting point to their data quality improvement objectives. Not only can they clean and prepare their data, but they can also make it a consistent part of their day-to-day routine at half the cost. Health care data must meet data quality standards as described above, which means healthcare institutions must implement a data quality framework that ensures uniformity, accuracy, and consistency. And they must meet these standards fast.
DME, being a data quality management solution allows users to profile, standardize, and clean billions of records from multiple data sources at record speed and accuracy. Moreover, with the ability to integrate over 500 data sources, users can directly update and amend their data sources without the hassle of third-party tools.
Conclusion – Help Your Organization Get Accurate, Reliable Data to Improve Patient Care Quality
To be useful, data must be correct, complete, reliable, and accurate. Flawed data leads to errors in decision-making, lethal mistakes in patient care (such as diagnosing the wrong patient), skewed numbers in research, and other critical problems.
While many healthcare facilities have collected data on patients, they have yet to develop up-to-date systems to maintain the quality of services provided. A self-service data quality tool as DataMatch Enterprise empowers authorized users to prepare data for its multiple uses without having to rely on IT or specific SQL expertise.
More importantly, it gives organizations a head start into the data improvement journey. Once the organization understands the problems affecting data quality, they can be in a better position to make necessary amendments, coming up with a more robust data management plan.
Download our free trial to see how you can clean and link your organization’s records the easy, code-free way.