Setting up electronic records management in healthcare: a guide

Setting up electronic records management in healthcare: a guide

June 30, 2022

Inga Shugalo

Healthcare Analyst

Electronic health records (EHRs) have already become an essential tool for most healthcare organizations across the US. According to the American Hospital Association (AHA), the vast majority of healthcare providers in different categories used EHRs from 2019-2021:

EHR across healthcare provider types, 2019-2021

At the same time, legacy EHR solutions are far from faultless, still riddled with insufficient interoperability and usability issues. Healthcare providers rely on EHR optimization to solve these issues or, in case the problem is serious, turn to IT vendors for EHR software development.

EHR can prove a valuable source of information about patient health, but to use it without causing harm, you need to ensure proper EHR data management. So how to do it with reasonable effort and time investments? We'll figure it out.

What is electronic records management?

Electronic health records management is a largely automated process encompassing a range of tasks related to the work with structured and unstructured data on patients’ health. These tasks include:

  • Comprehensive health data collection
  • Secure health information storage
  • Medical data processing
  • Patient health data analysis and review for errors

Electronic health records usually host large sets of structured and unstructured data that can help clinicians deliver the necessary personalized care to every patient.

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Electronic records management step-by-step

Step 1. Designing a data governance strategy

The data in the EHR should not be a dead weight, stored unused in the provider's systems or in the cloud. To ensure good performance of EHR systems, a data governance strategy is required.

What is data governance?

In the healthcare industry, data governance is a set of approaches that help providers achieve engaged participation from their staff and represent the interests of the entire organization in using data for making critical decisions. An important part of healthcare data governance is monitoring data quality to ensure that the organization successfully achieves its desired outcomes and gains business value from data management activities.

Besides, a well-designed data governance strategy also encompasses the ways a provider can share data with other providers while complying with HIPAA and ensuring their patients’ data safety. Additionally, such a strategy can foster the use of IT technologies and electronic records management software.

Step 2. Adopting a suitable EHR management solution

Several solutions can help convert paper-based health records management into an electronic format. First of all, these are tools for automated data entry, or virtual scribes.

Originally, scribes were clinicians’ assistants who worked with patient records during consultations. As digital technologies were adopted in the industry, scribes became virtual. Powered by NLP in healthcare and voice recognition technologies, these automated assistants have become a great asset for EHR systems, as they make EHR input stress and hands-free. Dictating electronic records data to virtual scribes, clinicians reduce inaccuracy and errors, which further speeds up the recording process. With such smart tools on hand, health specialists can overcome the notorious human factor.

Another solution that complements EHR management is clinical decision support software. With this tool in place, clinicians can prevent errors in prescriptions and medical histories. According to the FDA, the annual rate of medication errors is 400,000, but CDSS can prevent the majority of these cases.

As we know, the basis of CDSS are simple questions requiring yes-no answers. Developers can formulate rules regarding drug dosage relying on the standards and accepted norms and set up relevant rules in the solution. When operating, the tool activates the rules and alerts users in case a rule fails.

It is possible to integrate the solution with a health record system during EHR implementation or later on. This effort can help clinicians ensure proper Rxnorm coding for drugs, lab results with clearly set ranges, and correct detection of high-risk patients (also known as flagging), and more.

Step 3. Performing regular data cleanups

The quality of data in such EHR systems deteriorates with time, as some patients change their place of residence, name, provider, and so on. Incorrect and insufficient data in an EHR system can lead to inaccurate insights from analytics and a decrease in patient outcomes and satisfaction.

To prevent this, providers should regularly examine the EHR system, detect bad data and data gaps, delete faulty entries, and fill in the missing information. This is a time-consuming effort, so a provider might rely on professional data quality assurance services. Hiring third-party QA experts ensure that data cleaning is performed timely, accurately, and in compliance with the regulations.

Step 4. Adding social determinants

The pandemic has clearly demonstrated that it’s not doctors’ recommendations or the current health status that drives patients’ health behavior. According to the health impact pyramid, it’s socioeconomic factors:

Health impact pyramid

If socioeconomic factors are the cornerstone of health choices, it may be a good call to add them to the EHR solution to foster better analytics and the subsequent development of diverse engagement programs and initiatives that mitigate socioeconomic challenges for certain patient populations, helping them better manage their health.

Step 5. Training employees

Though EHR solutions simplify medical professionals’ work, the rate of satisfaction from using them among clinicians, nurses, and other medical workers is considerably low. According to the KLAS report EHR Satisfaction in Providers 2022, up to a third of clinicians, nurses, and other health professionals name EHRs among their burnout contributors:

EHR-related burnout in health professionals

So why is the EHR satisfaction rate so low? In fact, when working with an EHR solution, doctors have to deal with issues that hamper their daily clinical work. These are typically data errors and contradicting data on the same patients across different EHRs. To reduce such effects, it is necessary to conduct preliminary employee training, taking into account their level of computer literacy. You can also select expert users among employees to help others in learning the ropes of the EHR solution.

What’s more, it won’t hurt to employ communication technologies for EHR training. For instance, Vidant Health in Greenville, North Carolina, successfully resolved the complex task of providing EHR training to doctors traveling across care locations. Their method consists of video instructions centered around typical clinician workflows. Besides, clinicians attend three-hour-long, setting-specific (inpatient, ambulatory, or emergency department) classes, and those who work in multiple settings are required to go to each related class.

Electronic records management benefits 

As a result of having a solid electronic records management strategy in place, healthcare institutions note reaping the following benefits.

Error-free records

Unfortunately, errors in medical records are commonplace. They can occur across various care aspects, from patient billing and lab results to prescriptions and even drug dosage. What is more, according to a 2019 Kaiser Family Foundation (KFF) Poll, about 20% of participating patients noticed errors, while over 60% of doctors did not.

Common EHR errors

These errors can undermine patient experience and hamper patient-doctor relationships. Luckily, machine learning solutions in healthcare that make part of electronic records management tools can help mitigate these risks.

Coherent data across facilities

Hardly any provider employs a single EHR system across its locations, as, according to HIMSS Analytics, there are on average 16 disparate EHRs across all medical provider’s locations:

The number of EMRs per hospital

To consolidate the data, providers strive to improve EHR interoperability through health information sharing with partners and affiliates. At the same time, only 37% of providers are content with their data sharing efforts, as stated in Improving Health Care Interoperability: Are We Making Progress? report:

Health information sharing across providers

Electronic records management solutions can assist healthcare providers with data consolidation. Modern vendor-agnostic electronic records management tools powered by AI let clinicians share medical data even if they employ different EHR solutions.

Electronic health records consolidation is a typical task for care providers after a merger or a partnership agreement. This was the case with WellSpan Health, a healthcare system with over 200 care facilities located across central Pennsylvania and northern Maryland. Having merged with Summit Health, WellSpan Health detected potential gaps in medical records: patients interacted with providers using different EHR systems across the larger health system, which caused data quality deterioration.

To ensure due care quality, the WellSpan Health team had to consolidate patient information across the medical records systems to enable their doctors to access new location patients’ history and provide optimal care. But the clinicians couldn’t proceed with adding electronic records data immediately due to the differences in EHR standards accepted by the two clinics and adherence to outdated National Drug Codes (NDCs).

To save time and improve the quality of care, experts from WellSpan employed an AI engine. The tool ensured data cleaning and optimized large-scale data variations to reduce blank fields and avoid duplication. With these tasks completed, clinicians could employ electronic health records in clinical decision support at the point of care.

No duplicates or overlays

The two issues are commonplace in multi-location clinics and large healthcare networks. According to research by the American Health Information Management Association (AHIMA), 10% of health records in a typical US hospital are duplicates. Here are their detrimental effects on a provider’s work:

Duplicates effect on a provider’s operation

Duplicate health records, or clones, are multiple EHR entries for the same person. They often emerge in providers with multiple locations. Unlike duplicates, overlays can affect several patients’ data. Such a phenomenon happens due to overlapping data fields in the health records of two or more patients. These overlapping fields can be identical names, cities, or city districts. As a result, a new record appears mixing several patients’ personal details. The fact that a large hospital can serve several namesakes goes unnoticed.

Electronic records management solutions employ ML-powered patient matching algorithms to detect potential cases of duplicates, overlays, and other inconsistencies, and present them to experienced clinicians for further analysis. These clinicians cross-check the information and eliminate erroneous records if needed.

Thus, electronic records management solutions assist hospital staff with improving medical data quality and consequently, the quality of care and the patient experience.

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Closing thoughts

Today, EHR is considered a universal tool for storing information about patients, helping clinicians manage patients’ health and prevent adverse health events. Nevertheless, these tools still present difficulties for doctors due to faulty data – duplicates, erroneous entries, or blank spaces.

To use EHR solutions to the fullest, providers need to manage patients’ health data proactively. What is more, ML-driven electronic records management software can also assist with this task, detecting the upcoming issues automatically.

Still, providers need to remember that electronic records management is a step-by-step effort, and following the steps significantly reduces the time and effort health record management requires. These steps are:

  • Drafting a data management strategy
  • Enhancing the provider’s data management toolset with machine learning solutions – NLP-powered scribes, matching algorithms, and other machine learning tools
  • Regularly checking data quality independently or via a relevant QA service
  • Adding social determinants for improving clinical decision-making and the quality of services
  • Ensuring quality employee training

With such an approach, managing an EHR tool can become quite efficient and user-friendly.