The following is a guest article Ann Krutsko, ScienceSoft Healthcare IT Researcher
In healthcare, dealing with inaccurate patient data is a dangerous practice. Electronic health records (EHRs) were designed to streamline operations and improve coordination across health systems. Although EHRs have brought great benefits to organizations that have adopted them, there are still concerns about data accuracy. Human error is still common in data entry. However, we are seeing innovations in this area, including: Artificial intelligence integration We are making great strides in improving EHR data quality. From data validation to real-time alerts, see how AI is helping tighten the screws on healthcare data management.
Challenges in achieving data accuracy
Data accuracy in healthcare is literally a matter of life and death. Inaccurate data can put patient health at extreme risk when it leads to misdiagnosis, inappropriate treatment plans, and even fatal medication errors. But keeping medical data accurate is easier said than done. There are considerable hurdles that make this a difficult challenge for health systems.
- data fragmentation – Patient information is often spread across multiple platforms, hindering the collection of a complete and accurate medical history.
- human error – Busy medical professionals can make mistakes in data entry, diagnostic coding, or interpretation, especially in high-stress environments such as emergency rooms.
- lack of training – Rapid changes in software can outpace the training provided to staff and lead to mistakes in using electronic systems effectively.
- Cyber ​​security risks – Threats of hacking and unauthorized access can compromise data integrity, leading to inaccuracies in patient records or complete loss of records.
- Medical data complexity – Medical records contain intricate details such as medical history, test results, and medications and require accurate documentation. Even the simple act of incorrectly substituting a number can have a big impact.
The importance of data accuracy in EHRs
Many healthcare organizations are jumping on the EHR bandwagon. As of 2021, according to a report from the Office of the National Coordinator for Health Information Technology. 96% of non-federal acute care hospitals; 78% of employed physicians. We use certified EHR technology.
But it's not all smooth sailing. Although EHRs have many benefits, they also present unique challenges when it comes to data accuracy.
- patient safety – Inaccurate medication data can lead to harmful drug interactions. For example, if an EHR incorrectly lists a patient as not allergic to penicillin, they could be at risk for a severe allergic reaction.
- clinical decision making – A doctor may make a diagnosis based on test results stored in the EHR. Incorrect data, such as incorrect blood sugar readings, can lead to a misdiagnosis of diabetes.
- interoperability – When patients are transferred from primary care facilities to specialized cancer centers, data accuracy is essential to coordinate care and avoid costly or harmful duplicative tests and treatments.
- compliance – Accurate records are required to comply with regulations. If an audit reveals discrepancies or errors in the EHR, he said, healthcare organizations could face steep fines.
- quality of care – Inaccurate history of surgeries and medical interventions can affect future medical plans. For example, inaccurate records of past surgical implants can complicate future treatments and surgeries.
- cost efficiency – Incorrect labeling of lab tests in the EHR can result in unnecessary retesting, incurring additional costs and wasting valuable time.
- population health – Inaccurate vaccination records in EHRs can hinder efforts to control outbreaks of diseases such as measles and COVID-19.
- the study – Imagine a longitudinal study of heart disease based on EHR data. Inaccurate cholesterol or blood pressure measurements can invalidate study results.
- patient trust – If patients find errors in their EHR (such as incorrect medical history or incorrect test results), they may lose trust in their healthcare provider.
How AI can help
Although EHR systems are a great advancement, they are not completely secure when it comes to data accuracy. Artificial intelligence (AI) can fill the gap by identifying discrepancies, flagging errors, and helping keep healthcare data relevant.
data validation
The first line of defense is to ensure that the information entered into the EHR is as accurate as possible. AI is very good at data validation. For example, machine learning algorithms can automatically cross-reference new entries with existing data and flag discrepancies, such as different drug lists or conflicting test results.
Natural language processing (NLP) helps interpret and transform doctor's notes into standardized data fields. AI can also scan new entries for missing information and prompt providers to fill in all required fields.
automatic coding
Medical coding is a difficult business. This includes translating medical procedures, diagnoses, and equipment into standardized codes for billing and record-keeping. Because it is so complex, even experienced programmers are prone to mistakes, which can lead to billing errors, insurance issues, and impact patient care.
Automated coding systems can assign appropriate codes by reviewing medical records, test results, and physician records. This reduces the chance of human error and speeds up the coding process.
Anomaly detection
In the context of EHRs, AI algorithms can continuously scan data for outliers and irregular patterns that could indicate errors or fraud.
For example, if a patient's recorded blood pressure readings suddenly deviate significantly from historical data, it may be a data entry error or may indicate a serious medical condition that requires urgent attention. So the AI ​​can flag this and consider it. In billing and coding, anomaly detection can find inconsistent billing or coding patterns that could indicate mistakes or fraud.
real-time alerts
Imagine a doctor trying to prescribe a drug that may interact with another drug the patient is already taking. An AI system integrated with the EHR can immediately flag this potential interaction and alert the physician to choose a different medication or closely monitor the patient.
Alerts can also be applied to a variety of scenarios beyond drug interactions, such as identifying abnormal test results or flagging incomplete patient history. Real-time alerts powered by AI give organizations greater visibility into the data that matters most.
Investing in AI integration
You know that in the high-stakes world of medicine, every detail counts. It's great that you're already taking steps to implement electronic medical record systems. But why stop there? Investing in AI integration is the next logical step to fully protect data integrity. He takes the plunge and lets AI fine-tune his EHR system, because perfect data integrity could save lives.
About Ann Kruczko
With an eye for innovation, Anne applies strategic thinking and a deep understanding of the healthcare industry to develop solid strategies for ScienceSoft's growth in the healthcare IT space. Anne's research focuses on the needs of healthcare providers, medical device manufacturers, software startups, pharmaceutical companies, and other key market players to help them leverage technology to address their challenges.
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