Research Papers Collection

The Medical Records Pals Malaysia Research Papers Collection listed below is a collection of full text (in pdf format) Research Papers appearing in journals, which are primarily concerned with the research interests that explore theoretical and practical problems associated with broadly defined areas of health information management / medical records management.

This personal collection consists of health information management / medical records management associated / linked Research Papers from 2009 to the present. This collection will be updated periodically from my personal collection.

It is my fervent hope that these Research Papers will become an important forum for the discussion of research results and a source of original ideas.

It is also my hope that these Research Papers will stimulate a culture for research in health information management / medical records management.among Health Information Management (HIM) / Medical Records (MR) practitioners in Malaysia specifically and among HIM / MR practitioner readers of this website-blog outside Malaysia.

Click on the orange button below each subject in the list below which will open the Research Paper in a new tab of your current window. You can then choose to read online, print, or download for free the Research Paper and save it to your computer.

MEDICAL RECORDS PALS MALAYSIA RESEARCH PAPERS COLLECTION LIST

No.Subject Source
1.Medical errors in primary care clinics – a cross-sectional study
Khoo et al. BMC Family Practice 2012, 13:127
2.The Completeness of Medical Records to Assess Quality of Hospital Care: The Case of Acute Myocardial Infarction in a District level General Hospital in Iran
Assessment of Hospital Care Quality by Medical Records, Archives of Iranian Medicine, Volume 15, Number 10, October 2012
3.Barriers for Adopting Electronic Health Records (EHRs) by Physicians
Acta Informatica Medica, 2013 June; 21(2): 129-134 / Professional
4.Assessing the reliability of Causes of Death reported by the Vital Registration System in Sri Lanka:Medical Records review in Colombo
Health Information Management Association of Australia Journal, -http://dx.doi.org/10.12826/18333575.2013.0009.Rampatige
5.Written informed consent and selection bias in observational studies using medical records: systematic review
British Medical Journal, BMJ 2009;338:b866
6.Utility of a preoperative assessment clinic in a tertiary care hospital
Hong Kong Medical Journal, Vol 17, No 6, December 2011
7.Reflections on electronic medical records: When doctors will use them and when they will not
International Journal Of Medical Informatics, 79, (2010),1–4
8.Paper-Based Medical Records: the Challenges and Lessons Learned from Studying Obstetrics and Gynaecological Post-Operation Records in a Nigerian Hospital
TAF Prev Med Bull. Year: 2013, Volume: 12, Issue: 3
9.A Comparative Study of Laws and Procedures Pertaining to the Medical Records Retention in Selected Countries
Acta Inform Med, 20 (3), 174-179, doi:10.5455/aim.2012.20.174-179
10.Medical recordkeeping, essential but overlooked aspect of quality of care in resource-limited settings
International Journal for Quality in Health Care 2012; Volume 24, Number 6: pp. 564–567
11.Hospital payment systems based on diagnosis-related groups: experiences in low- and middle-income countries
Bulletin of the World Health Organization October 2013; Volume 91, pp. 746–756A

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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