This list (click on this link to open the list in a new tab of your current browser window) is a compilation of the Medical Records Department or Medical Records Unit webpage(s) for each hospital based on all the available websites for Government (Public) Hospitals, Ministry of Health (MoH) Malaysia.

The objective of the compilation is to facilitate readers :

to visit each webpage(s) of the Medical Records Department or Medical Records Unit of each hospital in this list
to compare each web page(s) layout and contents for the Medical Records Department or Medical Records Unit services of each hospital i.e its usability and the utility as after all, if users can’t use a feature, it might as well not exist; thus taking note of the following:
1. 1. if the webpage is indeed obvious and self-explanatory
  2. user requirements are kept to a minimum, thus a user-friendly service that requires almost nothing from the visitor and is unobtrusive and comforting
  3. effective writing, for example, using short and concise phrases – does it come to the point as quickly as possible, is used in the webpage
  4. does the webpage strive for simplicity instead of complexity
  5. the webpage provides consistency, for example, ensure the webpage uses the same language as the main website)
  6. the webpage’s screen layout, relationships, and navigability are all organised to provide the user with a clear and consistent conceptual structure
  7. the webpage presents the user interface that keeps in balance legibility, readability, typography, symbolism, multiple views, and colour or texture in order to communicate successfully
to stimulate inspiration to improve upon the creativity of designs for Government Hospital Medical Records Department or Medical Records Unit webpage(s) in Malaysia
to continue to strive to be as creative as possible, it is desirable that Government Hospital Medical Records Department or Medical Records Unit webpage(s) in Malaysia are designed to be functional and useful, but also aesthetically pleasing
to aim for standardisation in content and structure for all Government Hospital Medical Records Department or Medical Records Unit webpage(s) in Malaysia as the first, best step, and logical way to implement uniformity in content and structure throughout all MoH hospitals with a Medical Records Department or Medical Records Unit, if creativity of design is restricted or limited by the website administrator

The following inclusion and exclusion criteria are used for this compilation:

INCLUSION CRITERIA

the available official website for a hospital
available valid hyperlink to the Medical Records Department or Medical Records Unit webpage of each hospital from the available official website for a hospital

EXCLUSION CRITERIA

the available official website for a hospital but the website not available for display (during the period of this compilation)
the available official website for a hospital but without the following non-display of contents:
1. valid hyperlink to the Medical Records Department or Medical Records Unit webpage of a hospital including the following displays:
  1. blank webpage with just the title ‘Rekod Perubatan’ displayed
  2. ‘Forbidden’ error message
  3. ‘Under Construction notice
2. non-valid hyperlink to the Medical Records Department or Medical Records Unit webpage of a hospital including the following:
  1. the wrong webpage, for example, a webpage of another department or unit of the hospital
  2. no Medical Records Department or Medical Records Unit webpage
no available official website for a hospital

REVISIONS
This list will be updated periodically when new and valid Medical Records Department or Medical Records Unit webpage(s) are available. However, readers are kindly requested to notify me via email at vijayanr@yahoo.com of any corrections and /or additions to this list

Note:
‘Rekod Perubatan’ is the Malay (Bahasa Malaysia) term for Medical Records

References:

Ministry of Health Malaysia 2013, Listing Government Hospital. Available from: <http://www.moh.gov.my/gov_hospitals>. [17 October 2013]

EHR data and AI to predict response to antidepressant treatment

Posted on 27/04/2023 by VIJAYAN RAGAVAN

Antidepressants are frequently prescribed for adults with depression, a common and often disabling psychiatric condition. However, identifying the most effective treatment for a particular patient is often a trial-and-error process that can result in prolonged morbidity, disability, and exposure to adverse effects, as well as substantial healthcare costs. Precision psychiatry aims to optimise treatment matching using patient-specific profiles, but there are few evidence-based predictors available to clinicians initiating antidepressant treatment.

Although average response rates are similar across different antidepressant classes, individual responses can vary widely in clinical practice. Therefore, accurately and scalably guiding antidepressant selection presents specific challenges. The gold standard for characterising antidepressant response from electronic health records (EHRs) remains expert chart review, which is labor- and time-intensive.

However, advances in machine learning (ML) and the growing availability of large-scale health data, such as EHRs, offer new opportunities for developing clinical decision-support tools that may address this challenge. In a recent study published in the peer-reviewed open-access medical journal Nature Partner Journals (npc) Digital Medicine, researchers used machine learning models to accurately predict differential treatment response probabilities for patients and between antidepressant classes based on real-world EHR data. The pipeline incorporated AI and non-AI features, as well as unstructured data (i.e. clinical notes) to maximize the use of information contained in EHRs.

The study included 17,556 patients who received a new antidepressant prescription from non-psychiatrists, and data were obtained from 20 years of EHRs spanning from January 1990 to August 2018. The patients had at least one International Classification of Diseases (ICD) code for depression and at least one ICD code for non-recurrent depression during their history.

ICD codes from EHR data were obtained for adult patients (age ≥ 18 years) with at least one visit (the first visit with an antidepressant prescription is defined as the “index visit” for each patient) with a diagnostic ICD code for a depressive disorder (defined as ICD-9-CM: 296.20–6, 296.30–6, and 311; ICD-10-CM: F32.0–9, F33.0–9) co-occurring with an antidepressant prescription, and at least one ICD code for non-recurrent depression (ICD-9-CM: 296.20–6 and 311; ICD-10-CM: F32.0–9) any time during their history.

The resulting models achieved good accuracy, discrimination, and positive predictive value, which could be valuable for further efforts aiming to provide clinical decision support for prescribers. However, the researchers noted several limitations, including missing data in EHRs(e.g. patients who may receive some of their care outside of the healthcare system), and secular trends in clinician prescribing or documentation practices that may have affected model performance.

In summary, the study presents a novel computational pipeline based on real-world EHR data for predicting differential responses to commonly used classes of antidepressants. The approach demonstrated here could be adapted to a wide variety of other clinical applications for optimising and individualising treatment selection.

REFERENCES:

Sheu, Yh., Magdamo, C., Miller, M. et al. AI-assisted prediction of differential response to antidepressant classes using electronic health records. npj Digit. Med. 6, 73 (2023). https://doi.org/10.1038/s41746-023-00817-8