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Electronic Health Records (EHRs) adoption has increased in the last decade.There were many improvements in healthcare with the introduction of EHRs, like decrease of errors and misinterpretations, with improved healthcare quality and patient safety with the implementation and enforcement of safety guidelines and with the application of effective alerts in some uses cases. However, physicians are challenged daily with the inability to access relevant information using interfaces that are not user-friendly, the lack of interoperability and access among systems, the lack of ongoing, contextual training that keeps many in healthcare from optimizing the use of available systems, too many hours spent documenting care, less face-to-face interactions with the patient and burnout.
The growing data volumes and differences in terminology, language, abbreviations, and even spelling in clinical documents make it challenging for physicians to have a full picture of patients for clinical decision support.
A recent study demonstrates that 36 percent of clinicians spend more than half of their day on administrative tasks in the EHR. And what’s more, 72 percent of clinicians expect the time they spend on administrative tasks to increase over the next 12 months. Other study by American Medical Association (AMA), reveals that high volumes of data entry and poor usability are a main cause of physician burnout.
It is imperative to eliminate some of the redundancy and focus on what is meaningful. If the physicians cognitive bandwidth is used up just trying to get situational awareness, like, “Who is this patient, what are their meds, what are their problems, what are their care gaps?” then physicians are not able to be the diagnostician and to generate trust with patients.
“There is not a single silver bullet or a magic key for the documentation burden problem, there are complementary strategies than can improve both goals, improve documentation and time for the patient”
Several emerging technologies are attempting to address the challenges of clinical documentation, optimization is an integral part of leveraging the technology to improve clinical workflow. Simply because a practice has adopted and implemented an EHR doesn´t mean the hospital will automatically function more effectively. Understanding how to improve is key.
The speech recognition service enables physicians to easily and quickly dictate their clinical notes and see their speech converted to accurate text in real time without any human intervention. Artificial intelligence innovations marry data points, such as lab results, vital signs and clinical words in the patient record and provide clinical documentation suggestions to the provider. Computer-assisted analyses of physician documentation provide real-time feedback and attempt to resolve inaccuracies.
Tech giants like Amazon, Microsoft, Google are reaching deeper into healthcare with a new speech recognition service for clinical documentation.
One example is nuance’s integrations into such EHR systems as Epic and Cerner allow providers to dictate notes, use their voice to navigate EHR systems, and ultimately reduce the time it takes to provide reports to patients. By using automatic transcription in telehealth visits, providers can worry less about note-taking and focus more on the patient, while picking up on small cues that might otherwise be difficult to detect.
While Microsoft Azure Cognitive Services for Language, under the Cognitive Services umbrella, is a set of machine learning and AI algorithms for developing intelligent applications that involve natural language processing. Text Analytics for Health extracts and labels relevant medical information from unstructured texts such as physician notes, discharge recommendations, clinical documents, and electronic health records.
Stanford University is working with Google on a pilot project to study the use of a to replace a human scribe to save the physician time on data entry and improve physician-patient interaction.
Google is also working in a Cloud Healthcare API is a fully managed solution for storing, accessing, and analyzing healthcare data within the Google Cloud Platform (GCP) umbrella. The API comprises three modality-specific interfaces that implement key industry-wide standards for healthcare data: HL7 FHIR, HL7 v2, and DICOM. Each of these interfaces is backed by a standards-compliant data store that provides read, write, search, and other operations on the data.
Amazon Web Services announced the availability of Amazon Transcribe Medical, a new speech recognition capability of Amazon Transcribe, designed to convert clinician and patient speech to text. Amazon Transcribe Medical makes it easy for developers to integrate medical transcription into applications that help physicians do clinical documentation efficiently. It can automatically and accurately transcribe physicians’ dictations, as well as their conversations with patients, into text.
Part of Amazon Web Services cloud computing platform, Amazon Comprehend Medical is a natural language processing service that uses machine learning to extract health data from medical text, Part of Amazon Web Services cloud computing platform, Amazon Comprehend Medical is a natural language processing service that uses machine learning to extract health data from medical text.
However, while medical dictation software can help alleviate clinician burden, it is traditionally limited to the post-visit report, according to a review article published in Nature.
Technologies, workflows, and processes must enable clinicians with the data they need and when it is needed at the point of care. It starts with correct onboarding and training experiences enabling clinicians to use information systems properly. Additionally, the technology must be set up strategically and with flexibility to have data fed to clinicians in intelligent and intuitive ways to enable optimal workflows and achieve better patient care and safety.
Patient-generated health data (PGHD) support the ability to diagnose and manage chronic conditions, to improve health outcomes, and have the potential to facilitate more “connected health” between patients and their care teams, improving EHR use by supplying data to the physician, for example biometric data, symptoms, current problems or conditions, vital signs, side effects to a new medication, patient-reported outcome measures. This is data that the physician could not have obtained himself or herself, the two inevitably become partners in patient care, empowering the patient in their health.
While new technology approaches can help reducing the physician’s burnout, health system leaders should focus on how to create the conditions where joy, purpose and meaning are possible for physicians and care teams. Create a positive work environment, provide effective EHRtraining, reduce tasks that do not improve patient care, reduce burnout stigma, and improve counseling services, and create a national research agenda on clinician well-being.
Continuous improvement and agile software development and deploy are areas that could take advantages of this new emerging technologies and embedded them as soon and seamlessly as possible in the existing workflows. The era of monolithic solutions that usually take months or years to develop maybe are a thing of the past.