Disclaimer

If you require any more information or have any questions about this site’s disclaimer, please feel free to contact me by email at vijayanr@mrpalsmy.com

General disclaimers for www.mrpalsmy.com:

All the information on this website-blog is published in good faith and for general information purpose only.

www.mrpalsmy.com does not make any warranties about the completeness, reliability and accuracy of this information.

Any action you take upon the information you find on this website-blog www.mrpalsmy.com, is strictly at your own risk.

www.mrpalsmy.com will not be liable for any losses and/or damages in connection with the use of this website-blog.

From this website-blog, you can visit other websites by following hyperlinks to such external sites.

While  Medical Records PALS Malaysia and  I as the owner of this website-blog, strives to provide only quality links to useful and ethical websites, I have no control over the content and nature of these sites.

These links to other websites do not imply a recommendation for all the content found on these sites. Site owners and content may change without notice and may occur before we have the opportunity to remove a link which may have gone ‘bad’.

Please be also aware that when you leave this website, other sites may have different privacy policies and terms which are beyond my control.

Please be sure to check the Privacy Policies of these sites as well as their “Terms of Service” before engaging in any business or uploading any information.

Disclaimers for Joint Commission International (JCI) posts in Medical Records PALS Malaysia

This blog and the information and opinions it contains on the Joint Commission International (JCI) Accreditation Standards for Hospitals, 4th Edition (effective 1 January 2011) and the JCI Accreditation Standards for Hospitals, 5th Edition (effective 1 April 2014) have not been reviewed or endorsed in any way by JCI.While every effort was made by me, the author of this blog to ensure the accuracy and completeness of information in this blog when it was published, it should be used only for reference and guidance purposes.

Interested parties should contact your hospital management directly to obtain the most up-to-date information concerning solution offerings and functionality, and to discuss how they might be used to address specific JCI standards.

The blog posts regarding to JCI Standards is intended to serve and guide HIM / MR practitioners in Malaysia and those interested readers abroad in ensuring the quality of HIM / MR resources in their guardianship and custody, through posts that relate to quality standards for the management of communication and information in hospitals, regardless of the type of hospital they work at, irrespective if his or her hospital had acquired JCI accreditation status or one that is seeking JCI accreditation status or it is one that is not seeking JCI accreditation status at all.

CONSENT

By using this website, you hereby consent to my disclaimer and agree to its terms.

UPDATE

This site disclaimer was last updated on: Sunday, August 3rd, 2014. Should I update, amend or make any changes to this document, those changes will be prominently posted here.

Recent Posts

Voice-to-text medical software using NLP technology

When the doctor sits down with you on your visit, the doctor normally spends a lot of time inputting the how and the why of what’s happening to you, conventionally into a paper-based case note/medical record.

These free text narratives are further aggravated as not all doctors “speak the same way” in note creation and management.

These notes about your condition are rendered not easily extractable in ways that the data can be analyzed by a computer.

The good thing is this unstructured data of free text has given way to more and more ways to digital record-keeping—into the electronic health record systems (EHRs) way, away from the days of trying to decipher doctors’ medical lingo on hand written medical records and medical reports. However, EHRs are as unstructured patient data like its cousin, the paper-based medical record.

Inevitably, EHRs create challenges for doctors and that can be frustrating with additional data input responsibilities often bogged down by form-filling through the many clicks and screens required to navigate their EHRs, as well as they spending additional hours on updating EHRs.

EHRs became more important to be accurate and immediate with the scourge of the COVID-19 pandemic and with an increased reliance on contact-free consultations between doctors and patients.

Ultimately, huge volumes of unstructured patient data continue to be input into EHRs on a daily basis. As healthcare documentation is mostly unstructured, and it therefore goes largely unutilised, since mining and extraction of this data is challenging and resource intensive.

Medical Natural Language Processing (NLP) is steadily proving to be a solution to this challenge, creating new and exciting opportunities for healthcare delivery and patient experience. The adoption of NLP in healthcare is rising because of its recognized potential to search, analyze and interpret mammoth amounts of patient datasets.

Human beings use text and spoken words to fill up the human language with homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure, as some examples of ambiguities and irregularities as only they understand their usage.

NLP is a branch of artificial intelligence (AI) concerned with giving computers the ability to understand text and spoken words in much the same way we human beings can.

It is the main concept behind translation and personal assistance apps like Google Translate, OK Google, Siri, Cortana, and Alexa.

Without NLP technology using NLP healthcare tools capable of scrubbing large sets of unstructured health data, that data is not in a usable format for modern computer-based algorithms to easily access, extract, and accurately interpret clinical documentation of the actual patient record previously considered buried in text form.

NLP technology services accurately give voice to the unstructured data of the healthcare universe while processing the content of long chart notes of medical records, giving incredible insight into understanding quality, improving methods, and better results for patients that helps determine the disease burden and valuable decision support can be obtained.

Augnito is a voice-to-text medical software using NLP technology hoping  to improve healthcare, but for now specifically developed for the Indian market launched six months ago, and now being used in 24 States in India.

The voice has become the most powerful tool in technology today. Just by talking, the voice is the most natural way of communication for humans. We are able to do sophisticated and important jobs with gadgets like Alexa.

Like the Alexa gadget been able to do sophisticated and important jobs using voice controlled NLP technology, the Augnito software available for a monthly subscription on both Mac and Windows platforms, types out notes that are dictated to and saves it in an editable textual format on a cloud server.

The Augnito voice recognition software has a pre-programmed list of medical terms (its vocabulary database is constantly updated in keeping with doctors’ requirements and feedback), a built-in editor, report templates and keyboard shortcuts that help reduce repetitive typing.

Voice recognition software like Augnito using NLP technology, has the potential to boost a doctor’s productivity at a time of increased online consultations.

  1. Emergency use ICD codes for COVID-19 disease outbreak Leave a reply
  2. Global COVID-19 Clinical Characterization Case Record Form Leave a reply
  3. ICD Coding advice from the WHO for the 2019 novel coronavirus (COVID-19) Leave a reply
  4. ICD-11 2018 version: Part 2 – The ICD-11 Menu Hierarchy Leave a reply