IoT Healthcare Case Study : A Deep Dive

Problems that resulted in IOT adaptation

Data Management:

Thousands of records were scattered across the computer networks all round the world wide web–and keeping track of them was not easy. A system would need to be implemented that allowed disparate pieces of a patient’s life to be woven together into personalized care plans (Weil, 2016).

Operational Inefficiencies:

Hospital administrative operations were full of challenges, ranging from the placement of equipment to the movement of medical supplies. Delays in patient care were common (Weil, 2016).

Patient Care Delivery:

With patients demanding ever greater efficiency and quality to come from healthcare provision at their convenience. The basic challenge was how healthcare delivery could be delivered both more quickly and more to the individual (Weil, 2016).

Facing these challenges, Alberta Health Services has taken steps aimed at realizing a new dream of IoT technology. Alberta Health Services ’s determination in this field was as great as was their ambition (European Observatory on Health Systems and Policies et al., 2020). They were attracted to the prospect of being effective and economical, without being cumbersome, when compared with traditional software-based systems (Ketu & Mishra, 2021). They felt that the time had come to change healthcare delivery forever. This was no ordinary anecdote about people dying out (Weil, 2016).

After successfully implementing IoT solutions in delivering healthcare, Alberta Health Services did not know what effect the adoption of this vanguard system would have. Many are sceptical about medical care but the sales figures proved that they had supported something worthwhile on a grand scale– and there is no getting around the fact (Salehi-Amiri et al., 2022).

There is an integrated approach to quality medical care through high levels of personal care and management which only one kind company was able to offer. They have thus provided an ideal example for the future of medical treatment and have set a benchmark for the future of healthcare delivery (Alam et al., 2018)

Patient Monitoring

Advanced Analytics and AI Integration: 

Wearable IoT devices used for patient monitoring produce an enormous amount of health data. By integrating advanced analytics with artificial-intelligence algorithms, Alberta Health Services can handle this data more effectively, identifying patterns and predicting health issues before these potential problems escalate into something more severe (European Observatory on Health Systems and Policies et al., 2020). For instance, AI can also analyze heart rate variability at the same time as looking at vital signs and thus predict future cardiac events. This will lead to preventive medical intervention (Salehi-Amiri et al., 2022).

Impact on Chronic Disease Management:

For patients with chronic conditions such as diabetes or heart disease, real-time monitoring can significantly improve quality of life and outcomes. Real-time data allows for treatment regimens to be adjusted based on daily health variations, thus reducing the probability of emergency hospital visits and allowing for greater disease management in a personalized manner (Alam et al., 2018).

Asset Tracking

Integration with Hospital Information Systems:

The real-time asset tracking system is not just a standalone tool; it is also integrated into the hospital’s more broad-based information systems (Alam et al., 2018). This integration allows automated inventory management: when supplies of certain items are low or equipment nears the ends of its life cycle, the system can send a flag to procurement for more. This way, the hospital is always well-stocked and equipment maintained, upgraded or replaced as needed (Rowe et al., 2020).

Enhancing Patient Safety:

Patient safety can be greatly enhanced if the right equipment is available when needed. For example, tracking the sterilization and maintenance histories of surgical tools can reduce infection rates and making life-saving devices like defibrillators readily available can shave precious minutes off an emergency.

Smart Hospital Rooms

Personalization Through Machine Learning:

Smart hospital rooms are no longer limited to basic adjustments of environmental conditions. Machine learning could teach these rooms to remember patient preferences automatically, creating individualized, comfortable environments over time. For example, this might include everything from intensity of light and room temperature to which type of background music patients like best–all factors contributing toward a healthier, less hostile hospital experience (Salehi-Amiri et al., 2022).

Facilitating Patient Recovery:

Smart hospital rooms have benefits when it comes to patient recovery. It has been found that a patient’s environment can have a dramatic effect on recovery time. Patients who are in a relaxed environment can get well quicker-and recover better (Salehi-Amiri et al., 2022).

Remote Patient Engagement

Comprehensive Health Ecosystems:

Alberta Health Services ‘s remote patient engagement platforms are a component of a larger digital health ecosystem; in addition to connecting people directly to healthcare providers it can also connect them to Third Party health and fitness services (Rowe et al., 2020). This ecosystem proposes personalized health advice, pairs up with activity trackers so people can follow their exercise routine more frequently, and offers telemedicine health services–a total commitment to health and wellness.

Data Security and Privacy:

Deploying these IoT solutions requires careful consideration of data security issues and privacy. Alberta Health Services invests in advanced cybersecurity measures to protect patient data, comes in compliance with HIPAA (Health Insurance Portability and Accountability Act) regulations in the United States (Rowe et al., 2020). To ensure that patient information is safe from unauthorized access and stay that way during a security breach, secure data encryption, strong access controls, and regular security audits are the key (Salehi-Amiri et al., 2022).