Journal of Medical Research and Innovation (JMRI)
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Designing a Centralized Digital Platform to Support Type 2 Diabetes: From Idea to Implementation
Full text linkObjective: This early-phase digital initiative aimed to develop a centralized, user-friendly digital platform that aggregates resources for Type 2 Diabetes Mellitus (T2DM) self-management in British Columbia, Canada. The goal was to synthesize effective strategies from the literature to create an accessible resource for both patients and healthcare providers, thereby improving access to high-quality, publicly available T2DM educational materials.
Methods: A targeted literature review across PubMed, Cochrane, IEEE Xplore, and CINAHL identified essential components for T2DM digital platforms. We thematically synthesized a list of evidence-based features and design frameworks critical to this type of platform. Website development was co-led by patients and healthcare professionals through interviews (focus groups), usability testing (remote and contextual inquiry), and structured surveys with iterative feedback to ensure relevance and real-world applicability. Nine patients contributed lived experiences, while eleven clinicians provided expert input, shaping a digital platform that is both empowering and clinically sound.
Results: The literature review identified key features, including automated communication, interactive tools, goal tracking, educational resources, and professional support. A user-centered design approach ensured accessibility and alignment with user needs. The final platform integrated over 375 evidence-based resources, interactive features, and community support. This early-phase project recorded 1,368 website visits, 756 unique visitors, an average session duration of over 4 minutes (benchmark: 2–3 minutes), two pages per session (benchmark: 2 pages), and a 51% bounce rate (benchmark: ≤60%). These results underscore the platform’s role in enhancing health information access and user engagement.
Conclusion: This centralized digital platform for T2DM in British Columbia demonstrates the potential of user-centered, co-designed solutions to transform chronic disease management. By integrating resources, facilitating collaboration, and providing tailored support, this initiative addresses key gaps in diabetes care and exemplifies a scalable model for digital health innovation. Ongoing efforts will monitor long-term data to improve accessibility, expand content, and ensure continuous platform enhancement to better meet the needs of users and healthcare systems alike
Evaluation of hospital-based educational supports in the outpatient setting
Full text linkObjective: Children and youth with special healthcare needs (CYSHCN) in the United States face elevated stress from managing complicated treatment regimens with school outcomes that are generally worse compared to peers. As medical care is evolving towards increasing outpatient service delivery and decreasing hospital stays, CYSHCN have limited access to inpatient educational supports. Our team aims to describe the services in the expansion of a traditional inpatient Hospital-Based School Program (HBSP) to serve outpatient hematology/oncology, pulmonology, and dialysis clinics.
Methods: HBSP outpatient services began within outpatient hematology/oncology and pulmonology clinics followed by the dialysis clinic. Program changes focused on understanding current services, review and revision of data collection, promotion of service delivery standardization, and development of standardized hand off processes between inpatient and outpatient HBSP teachers.
Results: Across 2016-2020, 884 patients were served. Primary diagnoses included cystic fibrosis, leukemia, brain tumor, other cancer, lymphoma, dialysis, and blood disorders. A total of 80 counties in-state were served, and patients spanned 179 school districts. Out of 445 patients, 36.4% had an existing Individualized Education Program (IEP), 51.7% had an existing 504 Plan, and 11.9% were assisted with obtaining an IEP or 504 Plan.
Conclusions: Due to the HBSP, 884 patients received school supports. This showed that individuals who did have school supports received advocacy and a change in school services engagement with this HBSP. To our knowledge, this is one of the first studies to describe patient characteristics of individuals seen by an HBSP in outpatient clinics and the subsequent educational supports
Assessment of ‘Florence’ in Addressing Inquiries on Nicotine Replacement Therapy
Full text linkDear editor,
Artificial intelligence (AI) assisted chatbots, or conversational agents are new digital tools that mimic instantaneous human conversation. As AI assistants become more prevalent, evaluating their accuracy and consistency in providing health information is important. Evidence suggests that role of chatbots in smoking cessation is promising particularly in participant’s engagement.1 The World Health Organization has launched a digital health worker ‘Florence’ (a virtual human), powered by AI as its newest resource for providing the general population with accurate health information on COVID-19 vaccines and treatments, mental health, including smoking cessation.2
Background
Nicotine replacement therapy (NRT) is a widely recommended approach for smoking cessation to manage withdrawal symptoms associated with quitting smoking, such as irritability, cravings, and mood swings.3 It is recommended to use these products under the guidance of a healthcare professional.3 However, it is evident that people often search for information about addiction help-seeking queries from AI assistants.4 It is critical to understand the role and reliability of ‘Florence,’ especially in smoking cessation. The objective of this research was to elucidate whether ‘Florence’ provides evidence-based information in response to common NRT questions.
Methodology
A rigorous, methodical process was followed to develop an effective evaluation scale.5 The evaluation scale was developed to comprehensively assess the performance of an AI system across 3 parameters. In the first parameter, the AI evaluated on ‘voice recognition’ and ‘question understanding’. Voice recognition is scored on a scale from 0 to 2, where 0 represents a failure to differentiate between male and female voices, 1 indicates inconsistent recognition, and 2 signifies reliable recognition. Similarly, ‘question understanding’ is assessed on the same scale, with 0 denoting a lack of understanding, 1 representing inconsistent understanding, and 2 indicating consistent comprehension of questions. The second parameter was ‘consistency in answers between researchers’, the AI's performance is measured by answer consistency. Scores range from 0 to 2, where 0 signifies completely different answers between researchers, 1 suggests somewhat different answers, and 2 denotes identical answers. The third parameter, ‘accuracy of answers’, evaluated the AI's precision in providing correct responses. The scale ranges from 0 to 2, with 0 indicating completely inaccurate answers, 1 representing somewhat accurate answers with significant errors, and 2 signifying entirely accurate responses. The overall assessment is derived from the total score, where a cumulative score of 0-2 indicates ‘poor’ performance, 3-4 reflects ‘fair’ performance, 5-6 signifies ‘good’ performance, and 7-8 represents ‘excellent’ performance.
The scoring guidance was provided to support consistency across evaluators. We pilot tested this matrix before main data collection by collecting 20 questions from ‘Quora’ platform related to smoking cessation. Finally, the team had then read through the ACS FAQ webpage responses to evaluate consistency and accuracy and compare them with the responses from the ‘Florence.’
Fifty-six NRT questions were obtained from the American Cancer Society website.6 Two researchers independently queried ‘Florence’ and recorded the responses over a two-week period in January 2024. Responses were compared to the American Cancer Society answers to evaluate accuracy and between researchers to assess consistency. An 8-point rating scale was used across 3 evaluation parametres: voice recognition, question understanding, answer consistency, and accuracy.
Results
Out of 56 NRT questions asked, 11 questions (19.6%) were answered with excellent accuracy and depth of knowledge, demonstrating a strong command of the topics covered. Total 44 questions (78.6%) were rated as fair performance. Responses to these questions had some minor flaws in accuracy, comprehensiveness of information, or depth of explanation. There is room for improvement to address gaps in knowledge. Only 1 question (1.8%) received a poor performance rating. Approximately one-fifth of responses met excellence criteria, over three-fourths still have space for improving quality in content, detail, precision, or accuracy.
The results indicate a mixed performance of 'Florence' in addressing NRT-related queries. The identified gaps in knowledge, as evidenced by the ‘fair’ performance ratings, underscore the need for continuous improvement in the AI system. Addressing these gaps could enhance the quality of content, detail, precision, and overall accuracy of responses. As the field of AI-assisted chatbots in health information provision evolves, ongoing evaluations and refinements are essential to ensure these tools meet the highest standards in accuracy and reliability. This research contributes valuable insights that can guide future enhancements in AI-assisted health information tools, ultimately benefiting individuals seeking reliable guidance in their health journeys.
This study's strengths lie in its comprehensive evaluation methodology, real-world application, and actionable insights for improvement. Yet, limitations such as potential biases in comparison sources and subjectivity in evaluations emphasize the need for careful consideration in interpreting 'Florence's' performance.
Conclusion
In summary, while ‘Florence’ excelled in linguistic processing like speech and question comprehension, supplemental training focused on strengthening NRT knowledge itself would help address shortcomings in consistency, precision, completeness, and depth when answering domain-specific questions. Targeted improvement tuning both language mastery and core subject matter competencies could boost overall performance from fair to excellent across evaluations
Low back pain oswestry disability index changes following 8-week movement proficiency exercise program – A retrospective cohort study
Full text linkChronic low back pain (CLBP) is a worldwide epidemic, with a prevalence rate of 75–84% in developed countries. With the prevalence increasing, health-care professionals must question current best practice guidelines. In 2014, spinal neurosurgeon and back pain rehabilitation specialist Dr. David Johnson developed a unique back pain rehabilitation program referred to as NearoHAB®. The program’s uniqueness is founded on the principle that effective rehabilitation must eliminate the root cause of pain symptoms. The NeuroHAB® 8-week Movement therapy program aims to reverse movement dysfunction by restoring central nervous system-derived motor patterns based on proficient spinopelvic biomechanics for bending activities of daily living. To date, no other rehabilitation methodology adopts a movement dysfunction cause-based clinical model for back pain symptoms or includes a framework for what healthy lumbar pelvic movement should resemble. Over the course of the 8-week program, each participant is gradually upskilled, developing new default movement proficiency and improved biomechanics, in efforts to downregulate pain, improve disability, and increase functional movement capacity, creating a positive feedback loop for further progress. The leading question of this study is “How does functional movement-based therapy impact chronic low back pain?” Ten sets of participant details were selected at random and retrieved from the NeuroHAB® 8-week program database of 2020. All participants presented with CLBP, and two oswestry disability index (ODI) scores were documented – the first at the beginning of the 8-week program, and the second after the NeuroHAB® intervention. ODI scores were collated and the pre- and post-program results were measured and compared quantitatively through a paired t-test to determine the statistical significance of improvement. Results showed a two-tailed P=0.05 indicating that there was a significant difference between the pre- and post-data (0.0024). The pre- and post-group intervention ODI means were 25.80 and 13.30, respectively, resulting in a difference of 12.50 (95% CI: 5.73–19.27); determining the mean data between the pre- and post-intervention decreased by 48.4496%. The results from this study support the alternative hypothesis, concluding an 8-week intervention of functional movement therapy represented by NeuroHAB® results in a significant reduction of LBP ODI scores. 
Artificial Intelligence in Medicine: Revolutionizing Healthcare for Improved Patient Outcomes
Full text linkIntroduction: Artificial intelligence (AI) has emerged as a groundbreaking technology with the potential to transform various sectors, and the field of medicine is no exception. With its ability to process vast amounts of data and perform complex tasks, AI has begun to revolutionize healthcare, offering promising avenues for diagnosis, treatment, and patient care. In this editorial article, we will explore the significant impact of AI in medicine, highlighting its potential benefits and the challenges that lie ahead.
AI-Driven Diagnosis
One of the most remarkable applications of AI in medicine is its capacity to assist in accurate and efficient diagnosis. By leveraging machine learning algorithms, AI systems can analyze medical imaging, such as X-rays, MRIs, and CT scans, with a level of precision that rivals human experts. Studies have demonstrated the effectiveness of AI in detecting various conditions, including lung cancer, cardiovascular diseases, and neurological disorders, leading to earlier and more accurate diagnoses.
For instance, a study published in Nature Medicine by McKinney et al. revealed that an AI model trained on a large dataset of mammograms outperformed radiologists in breast cancer detection. The AI system achieved a lower false-negative rate and reduced the number of false positives, thereby potentially reducing unnecessary biopsies [1]. Similarly, a study by Esteva et al., showed that a deep learning algorithm outperformed dermatologists in diagnosing skin cancer based on images [2]. Such advancements in AI-driven diagnosis hold immense promise for improving patient outcomes and reducing healthcare costs.
Personalized Treatment and Precision Medicine
AI has also opened doors to personalized treatment strategies, enabling healthcare professionals to tailor therapies to individual patients. By analyzing vast amounts of patient data, including genetic information, medical history, and treatment outcomes, AI algorithms can identify patterns, predict responses to specific treatments, and recommend personalized interventions. This approach, known as precision medicine, has the potential to revolutionize disease management.
An example of AI's impact on precision medicine is showcased in the work of Poplin et al. The study demonstrated how a deep learning algorithm could predict the onset of cardiovascular events by analyzing electronic health records. The algorithm outperformed traditional risk models by incorporating a broader range of patient data, allowing for more accurate and timely interventions to prevent adverse events [3]. Similarly, Obermeyer et al., demonstrated that an AI model outperformed traditional methods in predicting acute kidney injury in hospitalized patients [4] while a study by Che et al., demonstrated the effectiveness of an AI model in predicting sepsis, allowing for early intervention and improved patient outcomes [5].
Enhanced Clinical Decision-Making and Workflow
AI has the capacity to enhance clinical decision-making by assisting healthcare providers in analyzing complex data and generating evidence-based recommendations. AI systems can process and interpret vast amounts of medical literature, patient records, and clinical guidelines, providing healthcare professionals with timely insights and decision support. This augmentation of human expertise can lead to more accurate diagnoses, improved treatment plans, and enhanced patient care.
A notable example is the work of Rajkomar et al., published in The New England Journal of Medicine. The authors developed an AI algorithm capable of predicting patient deterioration within the next few hours, based on electronic health record data. By alerting healthcare providers in advance, this AI system helped to prevent adverse events and facilitated proactive interventions [6].
Drug Discovery and Clinical Research
The drug discovery and development process is notoriously expensive and time-consuming. AI has the potential to accelerate this process by analyzing vast amounts of biomedical literature, genomic data, and clinical trial outcomes. Machine learning models can identify potential drug targets, predict drug toxicity, and optimize drug formulations. In fact, a study by Aliper et al., demonstrated that an AI system outperformed human researchers in designing new drugs to target age-related diseases [7].
Virtual Assistants and Telemedicine
AI-powered virtual assistants and chatbots are transforming the way patients interact with healthcare providers. These virtual assistants can provide instant medical advice, answer queries, and triage patients based on their symptoms. Furthermore, telemedicine platforms integrated with AI algorithms can enhance remote patient monitoring, enabling healthcare professionals to monitor patients' vital signs and provide timely interventions [8,9].
Challenges and Ethical Considerations
While the potential benefits of AI in medicine are substantial, it is important to address the challenges and ethical considerations associated with its implementation. Privacy and data security remain critical concerns when handling vast amounts of patient data. Maintaining patient confidentiality and ensuring secure data sharing frameworks must be prioritized to protect patient privacy.
Moreover, the need for transparency and interpretability of AI algorithms is vital to build trust between healthcare professionals and AI systems. Understanding how AI arrives at its recommendations or diagnoses is crucial for healthcare providers to make informed decisions and ensure accountability.
Conclusion: Artificial intelligence holds tremendous potential to revolutionize healthcare and improve patient outcomes. From enhancing diagnostic accuracy to enabling personalized treatment strategies and augmenting clinical decision-making, AI is transforming the field of medicine. However, to fully realize the benefits, it is essential to address the challenges surrounding privacy, data security, and algorithm transparency. By leveraging the power of AI responsibly, healthcare providers can usher in a new era of precision medicine, advancing the quality and effectiveness of patient care
Prevalence of mental disorders by sex among Hera General Hospital patients over the past 10 years
Full text linkObjectives: Mental disorders manifest as social, occupational, or emotional dysfunctions. Many countries struggle to recognize mental disorders and their effects on communities. Mental health awareness in Saudi Arabia has improved in recent years as psychiatric treatment has become more acceptable in Saudi society. The aim of this study was to determine the percentages of mental disorders among a hospital population at Hera General Hospital, Makkah, Saudi Arabia, using the diagnostic criteria of the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders and the tenth revision of the International Classification of Diseases. We aimed to determine sex differences and to identify the five most common disorders.
Methods and Materials: We identified clusters of mental disorders seen at Hera General Hospital psychiatric clinic using the diagnostic criteria of the DSM-IV and ICD-10 and it was a cross-sectional study of patients at a psychiatric outpatient department from July 2009 to June 2019 in Hera General Hospital, Makkah, Saudi Arabia.
Results: The most common mental disorders in patients attending the psychiatric clinic of Hera General Hospital were found to be major depressive disorder (41.3%), followed by anxiety disorders (22.1%), substance-induced psychotic disorder (11.4%), schizophrenia (8.9%), and Mental retardation (7.0%). Females were observed to have a higher risk for mood and anxiety disorders, whereas males had a higher risk for substance-induced psychotic disorder and schizophrenia.
Conclusion: Major depressive disorder was the most prevalent mental disorder at Hera General Hospital. Most patients with depressive disorder were female.
This paper was published by Scientific Scholar and has been archived here
Outcome of the first health skills simulation laboratory in the kingdom of Saudi Arabia
Full text linkSimulation advanced medical education and medical personnel evaluation across the world.[1] For improving the healthcare skills of medical staff and clinical performance of health-care practitioners, the Ministry of Health issued a budget of three million Saudi Riyals to establish the first clinical skills and simulation center in the Kingdom of Saudi Arabia, in Makkah, at the beginning of 2003. The objectives were to establish a state-of-the-art simulation laboratory to improve the clinical performance of health-care practitioners, facilitate consistent formal clinical training, overcome the difficulties encountered by practitioners in actual practice in either the pre- or post-graduate period, and provide jobs through specialized training and education.
The first phase of the project consisted of searching for a location, establishing a structured 3-year plan, and convincing stakeholders of the concept of clinical training. The second phase included preparation of the venue, building up of human resources, interior designing of showrooms, and documentation of each single action. The third and final phase consisted of marketing and advertising, official accreditation of short- and long-term courses, and postgraduate medical professional training. Each phase required an entire year of planning to complete from 2002 to 2004 [Figure 1].
Types of courses conducted at the skill laboratory since 2004: Anatomy and Physiology, Basic Cardiac Life Support (BCLS) and Advanced Cardiac Life Support, Pediatric Advanced Life Support and Neonatal Resuscitation Program, Advanced Trauma Life Support, Fundamental Critical Care Support, Clinical Nursing Skills, Difficult Intubation Course, Peripheral and Central IV Course, Gyn and PV Examination, Normal Deliveries and Its Complications, Diabetic Foot Care, Cardiac Catheterization, Middle Ear Diseases, Arthroscopy, Public Health Education, Upper and Lower GI Endoscopies, Bronchoscopy, and Endoscopic Retrograde Cholangiopancreatography [Figure 2].
As of 2022, the center is under the administration of Makkah Healthcare Cluster and named The Simulation Center of Makkah Healthcare Cluster, and it is affiliated with Hera General Hospital, Al Noor Specialist Hospital, Maternity and Children Hospital, and King Abdullah Medical City Simulation Centers. Each of the simulation centers has a vast majority of stations, with specialized stations related to the availability of medical specialty, for instance, Gyn/Obstetric-related stations at Hera General Hospital and Maternal and Children Hospital, Cardiac Catheterization station at Al Noor Specialist Hospital and King Abdullah Medical City, and Oncology-related stations at King Abdullah Medical City. The BCLS training centers at the institutions of Makkah Healthcare Cluster are supervised by The Simulation Center of Makkah Healthcare Cluster.
Figure 1. First purchase of the equipment in year 2002.
Figure 2. Types of courses conducted at the skill laboratory since 2004.
The Clinical Skills and Simulation Center at the Ministry of Health of Saudi Arabia has adopted various programs and specialized courses for its healthcare practitioners.[2] The vision of The Simulation Center of Makkah Healthcare Cluster is to render every simulation station accessible to its employees, with an interactive website to help provide the best experience in browsing through the courses that will be of immense support to the staff ’s field of expertise and to upgrade their knowledge and skills.
Authors contributions
Study conception, AK and MIF; manuscript review and editing, AK and MIF; manuscript writing, AAA.
 
A novel approach to combat the spread of coronavirus and other respiratory infections with the aid of a smart sanitizing respirator
Full text linkIn spite of social distancing, isolation at home, quarantine, use of face masks, and total shutdowns of cities, states, and countries COVID-19 has spread to infect nearly 150 million people and killed more than 3 million including many thousands of health care workers around the world in the course of 1 year. This invisible enemy like many other respiratory viruses spreads from person to person largely through airborne droplets or microdroplets.
If we had a respiratory device to wear that would disinfect and sanitize each breath before we breathe in and also disinfect and sanitize each breath we breathe out before it is discharged back into the air, we will be able to stop the airborne spread of respiratory infections. Such a device will obviate the need for total isolation at home and mass quarantines in ships or military bases. If you are sick with an acute respiratory infection, you will stay at home if the illness is mild or moderate and in the hospital if it is severe. If you have been exposed to coronavirus or suspected of having been exposed but have no symptoms, you should be able to go about your normal business while wearing this device. Same should apply if the symptoms are mild and/ or infection is with “flu” or common cold virus. The schools will stay open and there will be no need to close a town, city, a region, or an entire country. The anxiety and fear will be minimized. Health care workers will greatly benefit from this device and will not have to wear suffocating masks like N-95 respirator. During activities that pose high risk of aerosol transmission such as coughing, endotracheal intubation, bronchoscopy, suctioning, cardiopulmonary resuscitation, or disconnecting the ventilator, this device will provide near-total protection to the health care workers.
In the following paragraphs, I will describe the design of this device along with a conceptual sketch. I will also try to explain the structure and function of each component.
1. Face mask: It will be light and soft and pliable, preferably made out of transparent silastic. It will fit snugly over the mouth and nose allowing nearly zero air leak around it.
2. Connecting tubes: There will be two tubes coming out of the upper front part of the mask, and they will be labeled “Inspiratory line” and “Expiratory line.” They will go straight up over the forehead and curve around the frontal part of the skull going backward on the top of the skull. The inspiratory line will be connected to a one-way valve which will allow the air to flow in for inspiration. This valve will shut down during expiration. The expiratory line will be connected to a one-way valve which will allow the air to flow out during expiration. This valve will shut down during inspiration. Beyond the valve, each tube will be connected to a very light weight oblong flask which will be called “Sanitizing Chamber.” Each sanitizing chamber will be further identified as inspiratory or expiratory based on its connection.
3. Each sanitizing chamber will be about 6–10 inches tall with the shape of a flask and has an internal volume of about 1500 ml. In the center of each chamber, there will be a low-voltage light fixture holding a long thin bulb that will emit ultraviolet-c light (wavelength 250– 280 nm) when turned on. A battery cell will supply the power for this light. This light will continually sterilize the air around it inside the sanitizing chamber. The walls of the sanitizing chambers will be either made of aluminum or have a thin internal coating of aluminum to reflect the ultraviolet light all inside, to maximize its sterilizing effect.
4. The top of the sanitizing chamber will be the open end like the mouth of a flask. It will be a wide mouth and will be covered with a surgical mask, preferably made out of triple layer of cotton gauze. This mask will be changed every 8 h when in use. For extra safety during an epidemic or high flu season, double mask cover can be used.
5. The inspiratory chamber will have a side port with an on and off stopper on it. This port will be used for delivery of oxygen and/or medications if needed.
An average healthy adult male breathes in and breathes out about 500 ml of air with each breath and at rest, his respiratory rate is about 12–15 breaths/min. At the rate of 15 breaths/min, he takes in 500 ml of air every 4 s or so and exhales the same volume every 4 s. The sanitizing chamber of this respirator will hold about 1500 ml of air from which 500 ml will be inhaled every 4 s and replaced by the same amount of atmospheric air filtered through the surgical mask. If the sanitizing chamber was smaller and could hold only 500 ml, the air being inhaled would be exposed to ultraviolet light only for 4 s before being inhaled. This will be too short to achieve satisfactory sanitization. Ultraviolet light takes 10–15 s to sterilize the air in its vicinity.[1-5] With 1500 ml of air in the sanitizing chamber, each breath of 500 ml would have been exposed to ultraviolet light for at least 12 s before being inhaled. An average healthy adult female breathes in and breathes out about 400 ml of air with each breath at rest, and her respiratory rate is about 14–15/min. With mild exercise like slow walk during grocery shopping, it may go up to 18–20/min. So for women, a sanitizing chamber of 1500 ml will be sufficient and adequate. For men, sanitizing chambers of 2000 ml capacity will be better. It will be no problem to make these devices in two sizes. In fact, a third size with 2500 ml capacity sanitizing chambers for extra-large persons will not pose much of a problem to make. Similarly, two or three smaller sizes will be made for children.
There are some more advantages to using this device. The air going into the lungs of the user will be first filtered by the surgical mask and then sterilized by the ultraviolet light.[1-5] It will also enter the sanitizing chamber at a higher level where the air would be relatively clean and free of droplets hanging up in the air. Hence, if the user is healthy and is worried about catching an airborne infection like coronavirus, say in a mall or grocery store, he/she needs not worry at all because the air he/she will be inhaling, will have been sterilized in the inspiratory chamber with the help of ultraviolet-c light. [1-5] Moreover, if he/she were infected, he/she would not pose any risk to others because the air he/she would be exhaling would have been sterilized in the expiratory chamber before getting out. Since most people are healthy and will be using these respirators prophylactically, they will be sanitizing the air continually with each breath, thus helping the environment.
This respirator can provide some more therapeutic benefits. Through the side port of the inspiratory chamber, supplemental oxygen can be given easily without wasting any oxygen into the atmosphere as it happens with nasal cannula or a face mask. Someone with a respiratory rate of 20/min and tidal volume of 500 ml will be breathing in and out 10 L of air every minute. Oxygen through the side port of the inspiratory chamber at the rate of 1 L/min will supply 10% supplemental oxygen. At 2 L/min, it will go up to 20% and so on. In early stages of respiratory infection with coronavirus perhaps, this is all one would need in addition to other supportive measures. It will be possible to provide this kind of care at home to most patients, thus avoiding the need for hospitalization. Home health care workers, respiratory therapists, and nurses will be able to monitor the progress of such patients mostly through telephone and sometimes by home visits if needed. Patients and their families can be trained easily to check their vital signs and even oxygen saturation on daily basis and report to their health-care provider. Only very seriously sick patients requiring mechanical ventilation and/or other intensive therapeutic measures will need hospitalization.
Some more benefits will be realized from this respirator after it has been in use for some time. For example, it may be possible to deliver oxygen and/or other therapeutic agents into the bronchopulmonary segments with greater efficiency than the modalities in use at present. In the future, when some safe antiviral/antibacterial agents become available for pulmonary use, it will be possible to deliver them directly into the respiratory passages and lungs through the inspiratory chamber. With the help of this device, the treatment of other respiratory ailments such as bronchial asthma and emphysema might also become more efficient.
It will be easy to clean this respirator and all its components at the end of the day or as and when needed. The only disposable component is the surgical mask covering the mouth of each sanitizing chamber. Even that can be washed and reused, if it is made of triple layer of cotton gauze as recommended by me. The respirators used in the hospital to treat sick patients will of course be discarded and disposed of.
Will it be possible for this device to show some curative potential for patients with COVID-19 or flu or other respiratory infections? That will be a bonus to hope for. Only time will tell. It will, however, be quite possible and very likely that the number of flu cases each year will be reduced significantly with the help of this device, thus reducing the number of flu deaths also. That in itself will be a great achievement considering the yearly incidence and fatality rate of flu.
This respirator will make it possible for an “ambulatory isolation” in place of quarantine at home or some other place. Most workers will, therefore, be able to go back to work wearing this respirator. There will be no need to close manufacturing or meat processing plants. It seems that this respirator has the potential of altering the course of an epidemic with a respiratory virus and not only save lives but also the economy and the livelihood of millions of people.
This article was published by Scientific Scholar and has been archived here. 
Magnetic resonance imaging as a diagnostic tool for postpartum fistula-in-ano on episiotomy scar – A case report
No full textThe objective of this case presentation is to describe a rare case of fistula-in-ano at an episiotomy site and review the importance of magnetic resonance imaging as a diagnostic tool for the detection of perineal fistulas
The clinical and subjective outcomes associated with spinal manipulation: A case study
No full textSpinal manipulation (SM) has been documented to have various physiological effects, of which the research literature has started to reflect over the past decade. This case study was designed with intent to further investigate these findings. A 31 year old woman with experience of lifting weights and working a very physical job presented with ipsilateral right-sided lower neck and shoulder pain (C7-T4, right trapezius, and right scapula area) and bilateral low back pain (L1-L5 and S.I joint area). Following the examination, a differential diagnosis list was decided on with the input of multiple doctors and therapists. The primary treatment was SM over a time span of 6 months. The patient displayed significant results. Objective testing through a follow-up range of motion (ROM) examination showed an increase in ROM and a spinal examination presented a reduction in local muscle tightness. In addition, subjectively, the patient reported a significant reduction in pain, an increase in movement confidence, and ability. The results of this case study suggest that SM in conjunction with patient education has a significant positive effect on the patient’s reduction of pain, local muscle tightness and increase in ROM, and patient movement ability and confidence. Further studies are required to isolate the specific effects of SM in a high-powered study and clinical setting