SARIPALLI BHAGAT SAI REDDY

Abstract

We were attached to Changi General Hospital (CGH) for 3 weeks, and participated in a research study regarding sleep tests using polysomnography (PSG) by comparing manual scoring of PSGs and automation-assisted scoring.

Background information of the organisation

Changi General Hospital is Singapore's first general hospital for the east and north-east regions. With its logo of a blue cross identical to a “+” symbol, it represents hope for people who visit the hospital, aligning with their mission: To Deliver the Best Patient Care with Passion and Empathy.

The department involved in this research study is health services research and the Integrated Sleep Service.

The health services research department researches various aspects of healthcare services in CGH, which focuses on health economics to find cost-saving, effective and efficient alternatives that can better serve the patients’ needs. For example, our mentor working in this department has done research on various topics from the benefits of optical nasogastric tubes to the effects of using automatic gait analysis instead of manual analysis.

The Integrated Sleep Service in CGH is under the Changi Sleep & Assisted Ventilation Centre which offers comprehensive test, diagnosis and treatment services for patients with sleep disorders. This research study is focused on the sleep laboratory located at the Integrated Building, which also holds tests like the CPAP (Continuous Positive Airway Pressure) titration sleep study, MSLT (Multiple Sleep Latency Test) and MWT (Multiple Wakefulness Test), through updated sleep test devices.

Background information of the projects / tasks which I was involved in

Background of project

The focus of this research is on polysomnography (PSG), a clinical test used to diagnose sleep disorders. It does so by recording the patient’s brain waves, blood oxygen levels, heart rates and breathing rates, and eye and leg movements while they sleep.

In Changi General Hospital, it is conducted at the sleep laboratory where patients visit usually at night for the test to record their night time sleep patterns.

The cycle of falling asleep starts with non-rapid eye movement (NREM) sleep, At this stage, a person’s brain waves slow down. At this stage, the eyes do not move rapidly. At later stages of sleep, such as after 1-2 hours of NREM sleep, brain activity resumes and rapid eye movement (REM) sleep begins. A person typically experiences several sleep cycles at night, switching between NREM and REM sleep in around 90 minutes, but this can be disrupted by sleep disorders.

A PSG monitors a patient’s activities while asleep. The sleep technologist then interprets the activities such as by identifying the different sleep stages and cycles of the patient to make the appropriate diagnosis. However, this process is inefficient, as the sleep technologist has to manually score the data. Scoring the data of one patient for one PSG can take from 1 to 3 hours, depending on the complexity of data. Recently, an AI-based sleep stage classification system, Neurobit, has been created to speed up the process of interpreting PSG data by scoring it automatically. By helping to identify the different sleep stages of the patient’s sleep, it can greatly speed up the process of analysis. The software aims to speed up the analysis, but the sleep technologist is still required to check and properly diagnose the patient.

As such, Changi General Hospital aims to perform this research study to assess workflow improvements in terms of time and cost in diagnosing sleep disorders using the automated scoring software versus manual scoring by a sleep technologist. This is done by determining the effect of this system on various aspects contributing the workflow efficiency:

1. Cycle time to complete the manual scoring of a PSG by sleep technologist
2. Turnaround time between completion of a sleep test and corresponding PSG report being ready for review by clinician
3. Sleep technologist to patient ratio and overall workload capacity

Resources

The personnel involved in this research study include the sleep technologists in charge of scoring the PSGs, the respiratory sleep doctors, and health services researchers.

The budget for this study is approximately $10 000.

Elaboration / record of the activities done

Hypothesis

The automated scoring software is more efficient in interpreting the data than a sleep technologist

Methodology

To assess the workflow improvement, comparison of the efficiency of manual scoring and automated scoring has to be made. Therefore, we have to time the amount of time it takes for a sleep technologist to manually score a PSG (excluding disturbances such as attending to patients, picking up phone calls, etc.) as well as the time it takes to score a PSG with the aid of the automated software for the same PSG.

To maintain confidentiality of the patient as this research study does not include patient identifiers, only certain information of the patient is taken down. This includes the age, gender, race and sleep diagnosis (if applicable) of the patient. The sleep technologist’s age and number of years of experience is also recorded.

For auto-scoring by software, the following processes are timed:

1. Running-up laptop
2. Starting software
3. Transfer of data from hospital desktop to neurobit software
4. Neurobit data analysis
5. Transfer of data from neurobit software back to hospital desktop
6. Counter-check of neurobit data by sleep technologist

Assignment

This research study will last 6 months long. However, as this attachment only lasts for the first 3 weeks of the start of the study, we cannot complete the research study within this time period. Due to the time constraint, we can only complete the timing of manual scoring of PSG by sleep technologist.

Therefore, our assignment for the 3 weeks will focus on attaching to the sleep technologists and time how long they take to manually score a PSG.

We are seated near the sleep technologist as she scores the PSG, and we take note of the time where she starts, pauses, continues and stops scoring. Afterwards, the times are recorded in an excel sheet, and the total time taken for the sleep technologist to score that particular PSG is calculated.

Hence, during this project we were able to record the time taken by the sleep technologists but not the software. Hence, we were not able to compare the results and derive a conclusion. This step will be continued by the researchers at CGH.

Challenges

Some challenges we faced were that we could not precisely record the time where they pause or resume work, as we do not know the instant when they stop and start. We also had to assume that as long as they are looking at their computers, they are scoring the PSG. as we had no better metric to determine whether they were working In addition, we could not record very short breaks that last for a few seconds accurately. Due to all of this, we had to stay very focused on the sleep technologist.

3 content knowledge / skills learnt

Health economics

Professionals in the health economics sector are working on finding out about the social insurance frameworks in different sectors to find the best methods to use in the human services sectors, from diagnosis to treatment. Economic evaluation can examine on the productivity, viability and expenses of various medical services. This can then save costs in emergency clinics or facilities, maximising effectiveness.

Statistics

We also learnt about various statistical tests such as hypothesis test, T-test and chi square

Hypothesis Test

Hypothesis testing is used to test whether the data obtained from a sample population holds true for a larger population

In this test, the p-value is calculated which is the probability of finding the observed or more extreme results when the null hypothesis of the study is true. A value for alpha is also chosen which acts as a threshold value for p. If p-value < alpha, there is a significant difference and the null hypothesis is rejected. If p-value>alpha, there is no significant difference and the null hypothesis cannot be rejected.

T-test

T-test is used to determine if there is a difference in the mean between two groups.This is the primary test used in this research study, which aims to find the mean of the first group-- time taken to score the PSGs manually by the sleep technologist, and the second group-- time taken to score the PSGs with the aid of Neurobit. When the means are found, the T-test will be conducted to find out if there is a significant difference in the time taken, and a conclusion on whether to adopt Neurobit in scoring PSGs in CGH can be drawn.

Chi-Square test (χ² test)

Chi-square test measures and quantifies how the actual observed data from an experiment compares to the expected data.

Sleep disorder

PSG can be used to diagnose a variety of diseases as listed below

Obstructive Sleep Apnea (OSA)

One of the most well-known sleep diseases is called Obstructive Sleep Apnea (OSA). When this occurs:

1. There is blockage in upper respiratory system
2. Breathing halts for a few seconds while asleep
3. Soft tissues in pharynx relax, obstructs airway
4. Prevents oxygen from entering
5. Brain partially awakens to breathe
6. Cannot enter deeper sleep stages

In more severe cases, this can cause a strain on the heart, possibly resulting in heart attack, cardiovascular breakdown, and more over the long haul

Narcolepsy

Narcolepsy is a neurological issue whereby the cerebrum can't control the body's sleep cycle. Patients face ceaseless daytime drowsiness, causing them to nod off unexpectedly and during the day. This can influence the daily work of the patient and, in extraordinary cases, be dangerous, for example, if the patient were to be driving. Symptoms include excessive daytime sleepiness, sleep paralysis, cataplexy, hallucinations and more.

Restless Leg Syndrome (RLS)

RLS is a neurological issue where the patient has a need to move their legs while sleeping. This consistent need can severely affect the capacity of patients to rest, bringing about sleep loss. This generally prompts lack of sleep and can interfere with their daily routines.

2 interesting aspects of my learning

The first interesting thing we learnt was that at CGH’s office at tampines plaza, they have a group of people set up called the careline team. This is a programme funded by MOH. These people serve as the first line when someone in an emergency who urgently needs help calls cgh. These people will listen to the patient emergency and organise for the appropriate help to reach them as soon as possible, for example sending an ambulance. There is always someone who is available on this line 24/7. Hence, people often work night shift on this programme to ensure that someone is always available at all times to provide help

Another interesting thing we learned was about all the various research our mentor conducted before this research study. Some examples of such research that he conducted are the benefits of the use of optical nasogastric tubes, the impact of automatic gait analysis instead of manual and the impact of telemonitoring on glycemic control. In the research of the use of optical nasogastric tubes, he researched how the use of nasogastric tubes with cameras could help save lives by making it easier for the doctor to check that the nasogastric tube entered the right region in the stomach instead of incorrectly in the lungs. This also helps save time, as doctors will be able to check for this faster. As noted another research he conducted was the impact of automatic gait analysis vs manual gait analysis. In this research, he concluded that the use of software to automatically analyse the gait of elderly can help save time and money than when it is done manually only if the hospital if presented with more than 1 case per day. In his research about the use of telemedicine in glycemic control, he would that telemedicine used together with traditional methods can indeed help improve the state of glycemic control

1 takeaway for life

Artificial intelligence is assuming an inexorably significant role in human services, making medical services progressively effective and helpful for example Artificial intelligence assists with diagnosis and treatment of patients. For instance, telehealth and telemedicine allow clinicians to closely monitor patients from the convenience of their own homes.

Despite the dread of AI and automation, particularly it causing displacement of jobs, new innovation is still continuously being designed to assist us with our regular work, making things significantly more proficient and by and large precise. Right now innovation will just continue advancing in different fields of social insurance, an equalization must be found and we need to discover approaches to adjust. In the context of Singapore where the ageing population is causing an increase in illnesses, it is interesting to see the different ways technologies are used to aid us in combating these chronic illnesses.. By using economic evaluation, expenses can be reduced and benefits of healthcare services are maximised.

Consequently, our essential takeaway for life from this program is that we should stay aware of today's consistently developing world and continue refreshing our aptitudes to remain competitive.