If you listen to the gossip of the healthcare industry, you will hear that we are in a “post-EHR” world. In this world, everyone has an electronic health record that can be easily shared between providers, thanks to the health care institutions’ Require 21st Century Cure Act.
However, we may be ahead of ourselves because even in their own systems, users still cannot find what they want-and this situation will only get worse when data starts to flow in from other providers.
This is not a new revelation.In fact, I mentioned challenges in an article in 2017 interview Regarding the upcoming “data tsunami”. However, as data sharing between suppliers increases, the data tsunami has become more serious.
For a long time, users have been struggling to find relevant clinical information in their old version of EHR. Of course, clinicians can access question lists, drug lists, laboratory orders and results, doctor and nursing records, and other documents, but the detailed information is stored in multiple formats. Some data is recorded using standard terminology and code sets (such as ICD, CPT, LOINC, RxNorm, and SNOMED), but most information is also recorded in the form of free text annotations.
Because we continue to dump so much data in the EHR, clinicians are forced to search the patient chart section by section to find information about the diagnosis of a specific problem. Rather than asking clinicians to look around for data, it is better to provide clinicians with a better way to find the clinically relevant information they need to provide high-quality, cost-effective care.
The challenge of finding data in traditional EHR
One reason it is difficult for clinicians to find what they need in EHR is that most legacy systems are not designed to provide a diagnostic holistic view of a specific clinical situation. Let’s take renal failure as an example, it may be listed in the patient’s problem list. In order to assess the patient’s kidney status and determine whether the condition is effectively managed, the clinician must navigate to the drug list, then the laboratory results, and then the medical records. As the updated value-based care model rewards clinicians for cost-effective care and high-quality results, users need tools that can more easily find the specific information they need to make informed decisions.
EHR provides a wealth of clinical data, but clinicians need a way to access and use this data At the point of careIf clinicians want to see the details in the free-text encounter record, they don’t have the time—or maybe the patience—to open the EHR trash can, research in depth, and hope to find what they want.
What clinicians need is not trash can diving, but tools that allow them to select any item from the question list and immediately view clinically relevant information from all different locations and various formats in the EHR. One approach is to integrate a clinically relevant engine that can identify the relationship between diagnosis, medication, laboratory commands and results, and medical history and physical examination results, including mapping to relevant terms and code sets in each field, and then use diagnosis Filter to display relevant information at the point of care.
By adding such technologies to traditional EHRs, users can quickly find clinically relevant information that supports patient care and meets value-based care needs.
Data in Dumpster Dive 21Yingshi century
The emergence of the 21st Century Cure Act will exponentially increase the challenge of finding relevant data in the EHR. When the interoperability gate opens and providers need to send more information back and forth, the contents of one provider’s trash can will be added to another provider’s trash can. The clinician may be willing to reach out to find something in her own trash can, but she may be even more reluctant to search in other people’s trash cans.
If interoperability is to improve outcomes and promote the success of value-based care, new tools are needed to support the integration of relevant clinical data with specific documentation, workflow, and reporting requirements. However, some of the more widely discussed methods may not be able to solve this challenge.
For example, consider emerging technologies designed to work in the background to capture encounters and reduce the burden of documentation on clinicians. These solutions include environmental artificial intelligence, which uses microphones to capture sounds, translate conversations into text notes, and apply natural language processing to identify relevant clinical data. Although these tools sound promising, the error rate of these solutions is at most 10-20%, which means that someone must manually check the captured data to ensure accuracy. Imagine if the bank captures the sound, converts it into text, and then analyzes the results to determine how much money is in your account with a high degree of inaccuracy!
Another option is to use a scribe to record information about the encounter. Although this method helps to capture information about visits at the point of care, it does not necessarily create a record containing clean, structured data-this is essential to meet updated data and results reporting requirements, such as electronic clinical quality measurement (eCQM), and for the management of Medicare Advantage patients using classification condition codes (HCC).
Although environmental AI technology and scribes may help capture encounter information and even facilitate post-mortem analysis, neither of these two options can solve the data problem of dumpster diving: in other words, clinicians need to help users find them in the field Solutions for relevant clinical data. Points of care to drive better patient outcomes and cost-effective care.
One supplier approach
At the Phoenix Children’s Hospital, administrators hope to provide clinicians with an easier way to access important patient information. Clinicians want immediate access to key clinical indicators for each specific patient and their specific situation to support action at the point of care. They want to organize the information according to the way clinicians think and work, including a summary of each area of concern.
The organization also needs to integrate the solution into their old EHR. They adopted a clinical relevance engine, which includes workflow customization tools to drive patient-specific dashboards, actions, and documents.
This technology helps clinicians quickly find the information needed to manage and track patient care, and more importantly, improve patient treatment results. In some departments, doctors enter up to 97% of their notes in a structured format, enabling them to push clinical document data to a data warehouse that populates clinical dashboards and provides a visual representation of individual patients and patient groups. Clinicians do not need to use patient charts to understand their condition, but can understand the patient’s progress at a glance, identify any gaps in care, and make treatment decisions quickly.
With the implementation of the 21st Century Cures Act, clinicians will face more data that needs to be managed and interpreted. To save clinicians from the hassle of finding relevant data, organizations must adopt new technologies to work with their legacy systems and provide users with information about patients and specific issues they need at the point of care.
Photo: Filograph, Getty Images
