Electronic Health Records (EHR): How to Achieve Healthcare Data Accuracy with Artificial Intelligence

The following is a guest article by Ann Krutsko, Healthcare IT Researcher at ScienceSoft

In healthcare, working with inaccurate patient data is a dangerous game. Electronic Health Records (EHRs) were designed to streamline operations and improve coordination across healthcare systems. And while EHRs have largely benefited the organizations that have adopted them, data accuracy is still a point of concern. Data entry is still fraught with human error. However, innovations in the field in the way of artificial intelligence integrations are making significant strides in enhancing data quality in EHRs. From data validation to real-time alerts, let’s explore how AI is helping to tighten the screws in 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 if it leads to misdiagnoses, improper treatment plans, and even fatal medication errors. Yet keeping healthcare data accurate is easier said than done. There are quite a few hurdles that make this a tough task for healthcare systems:

  • Data Fragmentation – Patient information is often scattered across multiple platforms, hindering the assembly of a complete and accurate medical history.
  • Human Error – busy healthcare professionals may make mistakes in data entry, diagnosis coding, or interpretation, especially in high-stress environments like emergency rooms.
  • Lack of Training – Rapid changes in software may outstrip the training provided to staff, leading to mistakes in using electronic systems effectively.
  • Cybersecurity Risks – The threat of hacking or unauthorized access can compromise data integrity, introducing inaccuracies in patient records or complete loss of records.
  • Complexity of Medical Data –  Medical records contain intricate details, such as medical history, lab results, and medications, which require precise documentation. Something as simple as transposing a number incorrectly could have major implications.

The Importance of Data Accuracy in EHR

Many healthcare organizations have jumped on the EHR bandwagon. According to a report from the Office of the National Coordinator for Health Information Technology, as of 2021, 96% of non-federal acute care hospitals and 78% of office-based physicians have adopted certified EHR technology.

But it’s not all smooth sailing – while EHRs offer tons of benefits, they also come with their own set of challenges in the context of data accuracy.

  • Patient Safety – Inaccurate medication data could lead to harmful drug interactions. For example, if an EHR incorrectly lists a patient as not allergic to penicillin, they could be at risk of a severe allergic reaction.
  • Clinical Decision-Making – A doctor might rely on lab results stored in the EHR to make a diagnosis. Incorrect data, such as a flawed blood sugar reading, could lead to a misdiagnosis of diabetes.
  • Interoperability – If a patient is transferred from a primary care facility to a specialized cancer center, data accuracy is essential for coordinating care and avoiding costly or harmful duplicate tests or treatments.
  • Compliance – Accurate records are required for compliance with regulations. An audit that reveals inconsistencies or errors in an EHR could result in hefty fines for a healthcare institution.
  • Quality of Care – Inaccurate history of surgeries or medical interventions can influence future healthcare plans. For instance, an inaccurate record of a past surgical implant could complicate future treatments or surgeries.
  • Cost-Efficiency – A mislabeled lab test in the EHR could lead to unnecessary retesting, incurring extra costs and using valuable time.
  • Population Health – Inaccurate vaccination records in an EHR could hamper efforts to control outbreaks of diseases like measles or COVID-19.
  • Research – Imagine a longitudinal study on heart disease that relies on EHR data; inaccurate cholesterol levels or blood pressure readings could invalidate the study’s results.
  • Patient Trust – If a patient finds errors in their EHR—like incorrect medical history or misattributed test results—they may lose trust in their healthcare providers.

How AI Can Help

Even though EHR systems are a big step forward, they’re not foolproof when it comes to data accuracy. Artificial Intelligence (AI) can close the gaps by identifying inconsistencies, flagging errors, and generally helping to keep healthcare data on point.

Data Validation

The first line of defense is ensuring that the information going into the EHR is as accurate as possible. AI is exceptionally good at data validation. For instance, machine learning algorithms can automatically cross-reference new entries with existing data, flagging inconsistencies like differing medication lists or conflicting lab results.

Natural language processing (NLP) can help interpret doctor’s notes, converting them into standardized data fields. AI can even scan for missing information for a new entry and then prompt providers to complete all necessary fields.

Automated Coding

Medical coding is a tricky business. It involves translating medical procedures, diagnoses, and equipment into standardized codes for billing and record-keeping. Because it’s so complex, it’s easy for even experienced coders to slip up, which could lead to billing errors, insurance issues, or even impact patient care.

Automated coding systems can assign the right codes by sifting through medical records, lab results, and doctor’s notes. This reduces the incidence of human error and speeds up the coding process.

Anomaly Detection

In the context of EHRs, AI algorithms can continuously scan data for outliers or irregular patterns that might indicate errors or even fraudulent activity.

For example, if a patient’s recorded blood pressure readings suddenly deviate wildly from their historical data, AI can flag this for review, as it could be a data entry error or signify a serious medical condition that requires immediate attention. In billing and coding, anomaly detection can spot inconsistent charges or coding patterns that might indicate mistakes or even fraud.

Real-Time Alerts

Imagine a doctor is about to prescribe a medication that could interact with another drug the patient is already taking. An AI system integrated with the EHR could immediately flag this potential interaction, alerting the doctor to either choose a different medication or closely monitor the patient.

Alerts could also apply to a range of scenarios beyond drug interactions, like identifying abnormal lab results or flagging incomplete patient histories. With real-time alerts from AI, organizations gain an extra set of eyes on their most critical data. 

Invest in AI Integrations

In the high-stakes world of healthcare, you know that every detail counts. You’ve already taken the step to implement an Electronic Health Record system, which is great. But why stop there? Investing in AI integrations is your next logical step for bulletproofing your data integrity. Take the leap and let AI fine-tune your EHR system because impeccable data integrity could very well be a lifesaver.

About Ann Krutsko

With an eye for innovation, Ann applies her strategic thinking and deep understanding of the healthcare industry to create a solid strategy for ScienceSoft’s growth in the medical IT domain. Ann focuses her research on the needs of healthcare providers, medical device manufacturers, software startups, pharmaceutical companies, and other major market players to help tackle their challenges with technology.

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