Maintaining data accuracy is essential for all industries, but especially for healthcare, where information obtained from data or decisions based on it can literally save or risk lives. Healthcare organizations rely on data to improve clinical outcomes, administrative processes, and financial performance. However, as a vast amount of healthcare data is generated every second, inaccuracies like duplicate entries, missing values, and obsolete details become more prevalent.
According to a recent survey by Sage Growth Partners, “Healthcare organizations lose billions of dollars annually due to poor data quality. This includes the cost of rework, error correction, and the downstream impact of inaccurate data, such as misdiagnosis, overtreatment, and adverse events. If the healthcare sector works on data quality management, they can save up to $42.1 million in the next three years”. [Source: intersystems]
Let’s understand how inaccurate data is affecting the bottom line of healthcare organizations and why ensuring its reliability is crucial to providing improved patient care.
Implications of bad-quality data in healthcare
There can be several reasons leading to inaccuracies in the data, such as human typing errors during medical data entry, incomplete information scraped during data collection, and data obsolescence. Regardless of the cause, the impact of inaccurate data on healthcare organizations is severe, leading to:
1. Inaccurate diagnosis and treatment
Healthcare providers rely on complete and accurate patient data to make informed decisions about diagnosis and treatment. If they have access to inaccurate or incomplete data, they may misdiagnose patients or prescribe the wrong medications or treatments, which can have serious and potentially life-threatening consequences.
Pharmaceutical and life science companies also rely on high-quality healthcare data to develop and test new drugs and treatments. Real-world evidence (RWE) is a critical component of drug development, and to assess the safety & efficacy of a medicine, its RWE data must be accurate and complete. If the data is flawed, it can lead to misleading conclusions and potentially unsafe drugs or treatments being brought to market.
2. Wasted resources & employee inefficiency
Inaccurate healthcare data not only affects the patients but also the in-house staff. A significant amount of employees’ time is wasted in tracking and correcting inaccurate data and finding alternative sources of information. This impacts their operational efficiency and leads to frustration & less productivity. According to the same SGP survey, around 43% of the IT staff’s time is spent on data extraction, cleansing, and management. By cutting that time in half alone (by utilizing advanced data management techniques & automation, where possible), healthcare organizations can save around $1.6M in the next three years.
Additionally, when employees feel they can’t trust the data they are provided, they may lose trust in the leadership team, making it difficult to implement new initiatives and changes.
3. Ineffective analysis & decision-making
Stakeholders and policy-makers who rely on data for decision-making can be adversely affected by inaccurate information. To make informed decisions, they rely on aggregated datasets, which can become corrupted by minor errors during medical data entry or collection. Over time, these errors can multiply and propagate through downstream systems, leading to inaccurate records that can misinform decision-makers. They can end up making poor decisions about patient care, resource allocation, and other important matters, having negative consequences for patients and the organization.
4. Financial loss
Every year, healthcare organizations lose a significant amount of their revenue due to inaccurate or poor-quality data, in several ways, including:
- Lawsuits: If inaccurate data leads to patients receiving incorrect treatment/medication that adversely affects their health, they can file a lawsuit against the healthcare firm. If the patient wins the case, the healthcare firm will be responsible for paying damages and bear a huge loss.
- Uncompensated care: If a hospital has inaccurate data on patients’ charge entry, they can end up charging less to patients. This can lead to significant financial losses, as the healthcare firm will not be reimbursed for the provided services.
- Duplicate payments: If there are duplicate records of patients’ invoices, the accounts payable department can end up paying twice for the same service, leading to significant financial loss.
- Ineffective marketing: If the marketing data is incomplete or inaccurate, healthcare organizations can end up wasting lots of money on ineffective marketing campaigns with poor ROI.
- Claim denials: If a healthcare firm submits inaccurate treatment data of patients to the insurance company, the chances of claim denials are higher. This can lead to significant financial losses to healthcare organizations, as they will not be reimbursed for the services they provide.
5. Non-compliance with data regulations
Maintaining accurate healthcare data is also crucial for such firms in order to comply with the various data regulations applicable in their countries/regions and avoid legal repercussions.
For instance, maintaining accurate healthcare provider data is crucial for health plans and workers’ compensation organizations to comply with the No Surprises Act – part of the Consolidated Appropriations Act of 2021, which demands accurate healthcare data for public awareness. NSA states that if there is any change in the health provider’s data (such as a change in network status or contact information), insurance firms must update it accurately within 48 hours of the change to keep patients informed. If they fail to accurately update the details of their healthcare providers or comply with regulations, they can be fined and sued.
Real-world examples of the impact of poor-quality data on healthcare
To justify what we have stated above, here are some real-life examples of how inaccuracy in healthcare data impacts medical organizations on the ground level:
1. OhioHealth’s decision-making process flawed due to data accuracy issues
OhioHealth is a renowned non-charitable healthcare organization that has been collecting medical data from disparate sources for decades. But all of their data was scattered & stored in various silos with plenty of inaccuracies, making it difficult for policy-makers to understand what was best for the organization and make informed decisions.
The organization realized that a data warehouse could help them improve their data quality by bringing all the data together in one place and eliminating duplicates and inaccuracies. However, for efficient utilization of the data warehouse, it was crucial to establish a data governance framework that talks about how to manage data. For OhioHealth, it was initially challenging to put data governance principles into practice, but eventually, it improved the data quality in healthcare and made it easier to implement the data warehouse.
2. The US government identified medical errors as the third-leading cause of death
According to the British Medical Journal, medical data errors are a leading cause of death in the US, with an estimated 250,000 deaths each year. This is because many healthcare organizations have large amounts of patient data that is incomplete, inaccurate, or outdated, leading to patient misidentification, incorrect diagnoses, and improper treatment.
To address this issue, the Department of Health and Human Services (HHS) has developed a Patient Demographic Data Quality (PDDQ) framework. This framework provides healthcare providers with a set of 76 questions they can use to assess their data quality standards & procedures and improve them. By implementing this framework, organizations like Kaiser Permanente witnessed improvement in their patient care in less than three weeks.
How to meet data quality goals in your organization for improved patient care & business operations?
To ensure that your healthcare records meet data quality goals (accuracy, validity, completeness, consistency, and timeliness) and regulatory compliances, here are a few ways you can consider:
- Invest in an Integrated Data Analytics system & advanced data quality management tools that help you reduce errors, improve data governance, automate data processing workflow, and analyze actionable information in datasets for informed decision-making.
- Set a data governance framework for your organization to determine how the data will be collected, managed, stored & accessed, and train your staff to ensure they adhere to them while working with big data.
- Implement a data profiling strategy to determine a consistent format for data management, analyze data collection sources, and uncover & rectify inaccuracies & inconsistencies in the data. Organizations can leverage a combination of tools, algorithms, and business guidelines to assess the quality of their data and improve it for analysis & other purposes.
- Conduct regular data audits in your organization to assess the effectiveness of your data quality management strategy and identify areas for improvement. It also helps you discover new data in your database, and its data sources, and assess its reliability, accuracy, and freshness. The frequency of data audits (monthly, quarterly, or bi-annually) will depend on the size and complexity of the organization’s data environment, as well as the rate at which data is changing.
- Outsource medical data entry services to a trusted HIPAA-certified provider. These providers have the expertise and dedicated team of data experts to collect, clean, structure, and process your vast and sensitive data correctly & efficiently. Utilizing this data, you can make effective decisions for improved patient care. Outsourcing is a viable option when you want to free up your resources for patient care, instead of investing their time in data collection, error identification & rectification, and management.
Key Takeaway
There is no doubt that inaccurate data is a serious concern for healthcare organizations, costing them millions and putting patients’ lives at risk. Since, the majority of inaccuracies occur at the initial stage, i.e. during medical data entry or collection, it is critical for organizations to leverage advanced data scraping techniques with human experts to minimize errors. At the same time, a data governance framework is required to maintain data quality at all stages.
Healthcare organizations can enhance the utility of their data by investing in the right resources and tools, or by outsourcing medical data management services to a qualified provider.