Malaysia’s National Policy for Quality in Healthcare 2022 – 2026

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A National Policy for Quality in Health Care sets out a Government’s primary objectives to assure quality in health care and continuously improve the care. 

We know that quality is never an accident, always the result of high intention, sincere effort, intelligent direction, and skillful execution. Moreover, it represents the wise choice of many alternatives.

The Malaysian Institute for Health Systems Research (IHSR), in a partnership with the Malaysian Society for Quality in Health (MSQH), jointly and successfully organised to an overwhelming response to participate in the National Seminar for Quality in Healthcare free virtual seminar on October 5, 2021.

It comes when the public health care system is in dire need of refocusing its collective efforts towards improving the quality of care provided for the whole health system – public and private.

Highlights of the virtual seminar included the launching of the National Policy for Quality in Healthcare(NPQH) and a keynote address by Tan Sri Dato’Seri Dr. Noor Hisham Abdullah, the Director-General of Health Malaysia, Ministry of Health Malaysia(MoHIM). He, in turn, launched the National Policy for Quality in Healthcare document.

This day-long seminar had many distinguished Malaysian speakers, including Dr. Samsiah Awang, Head, Centre for Healthcare Quality Research Initiative for Health Systems Research(IHSR), Datuk Dr. Kuljit Singh, the President of the Association of Private Hospitals Malaysia(APHM), Dr. Nor’ Aishah Abu Bakar, Deputy Director, Medical Care Quality Section(MoHM), and Dr. Fadzilah Shaik Allaudin, Senior Deputy Director of the Planning Division(MoHM).

Distinguished invited speakers were the Director, HEALTHQUAL, Institute for Clinical Health Sciences, UCSF, Prof. Dr. Bruce Agins, Dr. Shams Syed, and Ms. Nana A. Mensah-Abrampah. Both Shams and Nana are from the Department of Integrated Health Services, World Health Organisation, Geneva.

The many exciting topics represented ranged from National Quality Policy and Strategy: The Global Imitative/Perspective, National Policy for Quality in Healthcare for Malaysia: Where Are We Heading?, Clinical Governance and its impact on Quality in a Private Hospital, Safety and Quality in Digital Health and Innovation, Malaysian Patient Safety Goals 2.0: Concise and Practical, Compassion: The Heart of Quality and Improving Quality of Care in Resource-Limited Settings.

You may view a flipbook of the National Policy for Quality in Healthcare from (this link will open in a new tab of your current browser window) http://library.nih.gov.my/e-doc/flipbook/npqh-2022-2026/index.html

As I see it, this Malaysian version of a National Policy for Quality in Healthcare document attempts to provide all public and private health officials with the strategic direction they need to follow to assure quality in health care and continuous improvement in the healthcare provided in Malaysia. 

Analysis of mortality and cause of death data using ANACoD3

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During a part of the continuing WHO ICD-11 webinar series,  in collaboration with the Surveys, CRVS, & Health Service Data Unit, the Classifications and Terminologies Unit of the World Health Organisation (WHO), launched the Analysing Mortality and of Cause of Death 3 (ANACoD3) on September 29, 2021.

ANACoD3 is a new electronic online tool that helps to perform a comprehensive and systematic analysis of mortality and cause of death data.

References: WHO ICD-11 Webinar series – ANACoD3 tool launch, available online{link opens in a new tab of the same window): https://www.who.int/news-room/events/detail/2021/09/29/default-calendar/who-icd-11-webinar-series—anacod3-tool-launch

Voice-to-text medical software using NLP technology

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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.

Global COVID-19 Clinical Characterization Case Record Form

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In the wake of COVID-19, I have been thinking how coronavirus data is been captured into a typical medical record. A check around the Internet led me to the World Health Organisation [WHO] recommended rapid clinical characterisation case record form (clinical CRF).

Like the one standardised form i.e. The World Health Organisation (WHO) International Form of Medical Certificate of Cause of Death to collect mortality data among member states—with the clinicial CRF form also by the WHO, the WHO intends that by using one standardised clinical data tool, there is potential for clinical data from around the world to be aggregated; in order to learn more to inform the public health response and prepare for large scale clinical trials.

This form is intended to provide member states with a standardised approach to collect clinical data in order to better understand the natural history of this disease and describe clinical phenotypes and treatment interventions (i.e. clinical characterisation) for Covid-19.

Some important stuff to take note if implementing this form include:

1: this CRF has 3 (M)odules to be completed—(M1)for first day of admission to the health centre, (M2) on first day of admission to ICU or high dependency unit, also be completed daily for as many days as resources allow and continued to follow-up patients who transfer between wards, and (M3) to be completed at discharge or death; and,

2: Internet services are required to enter data to the central electronic REDCap database or to your site/network’s independent database; the form guidelines suggest that printed paper CRFs may be used and the data can be typed into the electronic database afterwards.

The form can be viewed from the link (the link will open in a new tab of your current window) in the reference given below.

Reference:
Coronavirus disease (COVID-19) technical guidance: Patient management, Case Management, WHO, <https://www.who.int/docs/default-source/coronaviruse/who-ncov-crf.pdf?sfvrsn=84766e69_4>