Healthcare Related Laws

The list below is an alphabetical order list of Malaysian laws which may directly or indirectly affect healthcare in Malaysia. This list will be updated as and when necessary.
References:
MyLawyer.com.my, 2013, viewed 9 February 2013, <http://www.mylawyer.com.my/index.php>

ALPHABETHICAL LIST OF THE LAWS OF MALAYSIA (LOM) WHICH MAY DIRECTLY OR INDIRECTLY AFFECT HEALTHCARE IN MALAYSIA

No.LAWS OF MALAYSIA (LOM)
1Births and Deaths Registration Act 1957 (Revised 1983)
2Care Centres Act 1993
3Census Act 1960 (Revised 1969)
4Child Act 2001, Act 611
5Child Care Centre Act 1984
6Child Protection Act 1991
7Children and Young Persons Act 1947
8Computer Crimes Act 1997
9Dangerous Drugs Act 1952 (Revised 1980)
10Dangerous Drugs (Forfeiture of Property) Act 1988
11Dental Act 1971
12Destitute Persons Act 1977
13Destruction of Disease-Bearing Insects
14Digital Signature Act 1997
15Drug Dependants (Treatment and Rehabilitation) Act 1983
16Employees’ Social Security Act 1969
17Evidence Act 1950, Section 90A
18Fees Act 1951 (Revised 1978)
19Human Tissues Act 1974
20Malaysian Health Promotion Board Act 2006
21Medical Act 1971
22Medical Assistants (Registration) Act 1977
23Medicines (Advertisement and Sale) Act 1956 (Revised 1983)
24Mental Health Act 2001 (Not yet in force)
25Midwives Act 1966 (Revised 1990) Military Manoeuvres Act 1983
26National Archives Act 1966
27National Archives Act 2003
28National Registration Act 1959 (Revised 1972)
29Nurses Act 1950 (Revised 1969)
30Occupational Safety and Health Act 1994
31Optical Act 1991
32Personal Data Protection Act 2010
33Pesticides Act 1974
34Private Healthcare Facilities and Services Act 1998
35Private Hospitals Act 1971 ( Repealed by Act 586 )
36Registration of Births and Deaths (Special Provisions) Act 1975
37Registration of Pharmacists Act 1951 (Revised 1989)
38Sale of Drugs Act 1952 (Revised 1989)
39Sale of Drugs Act 1952 (Revised 1989)
40Sewerage Services Act 1993
41Telemedicine Act 1997 (Not yet in force)

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.

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