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Ricky Philip
Ricky Philip

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MedTech: Leveraging Sensor-Generated Data To Reform Clinical Decision Making

In today’s healthcare environment, patient safety has never been more important than it is now. The rise of electronic medical records (EMRs) and the adoption of new technologies such as the Internet of Things (IoT) have created a significant amount of data that can be used to improve patient care. As the medical field continues to become more data-driven, the use of sensor-generated data will become an integral part of clinical practice.

In order to make optimal decision-making possible, it is important to leverage sensor-generated data for clinical decision-making in healthcare. Sensor-generated data refers to all types of data collected from connected sensors (e.g., medical devices) with an aim to improve patient care outcomes or monitor processes in healthcare settings. It includes data from devices such as blood pressure cuffs, ECG monitors, pulse oximeters and thermometers, and wearable sensors such as accelerometers and gyroscopes that provide information about movement patterns during daily activities such as walking or running.

However, clinicians often overlook these new forms of information because they do not know the multiple benefits and how to analyse them effectively.

The Growth Of Sensor-Generated Data

The growing use of sensors in healthcare drives the need for real-time data analytics. The healthcare industry is expected to generate more than $3.5 trillion in revenue by 2025 from the use of electronic health records, mobile health applications and wearable technology, according to a report by McKinsey & Company’s Health Care Global Institute. This growth has been driven by the need for better decision-making that can be used as an outcome measurement and cost-savings opportunities.

The healthcare industry is under a lot of pressure. The need to reduce costs, improve quality and expand access to care has resulted in a rapid shift towards value-based care.

In order to address these challenges, organisations must adopt new technologies that can help them achieve their goals. But the adoption of technology is not enough. It's important for organisations to understand how these technologies can be used to make real-world improvements in patient outcomes and business processes.

One way that organisations can leverage sensor-generated data for clinical decision-making is through the use of predictive analytics. Predictive analytics allows organisations to anticipate future actions based on past experiences. Sensors are devices that collect data from within the body or environment and provide real-time feedback on physiological processes. They may include vital signs such as blood pressure, temperature and heart rate; movement sensors that track physical activity; or physiological sensors such as accelerometers that monitor sleep cycles.

Sensors enable patients to self-monitor their health status using wearable technology like smartwatches or activity trackers. These devices allow clinicians to capture valuable information about patients' health status anytime without requiring them to come into the clinic for regular checkups or laboratory tests. In addition, they can provide insight into how certain lifestyle factors affect patient health.

For example, if patients are being discharged from the hospital with an expected length of stay of three days and then spend five days there instead, an organisation might adjust its discharge planning strategy accordingly. This approach would allow for better use of resources within hospitals while also reducing costs associated with unnecessary readmissions by reducing unnecessary admissions into long-term care facilities (i.e., nursing homes).

Benefits Of Leveraging Sensor-Generated Data For Clinical Decision Making

The benefits of leveraging sensor-generated data for clinical decision-making in healthcare are numerous:

*1. Increased accuracy: *
Patients who receive care from clinicians who leverage sensor-generated data may experience fewer complications and shorter hospital stays, which translates into better patient outcomes.

*2. Increased transparency: *
Clinicians who leverage sensor-generated data can provide patients with real-time updates about their health conditions or treatment plans, leading to greater trust between patients and clinicians.

*3. Improved efficiency: *
The ability of clinicians to make informed decisions based on real-time data from IoT devices can save time and money for patients and hospitals alike.

Top Use Cases Of Leveraging Sensor-Generated Data To Reform Clinical Decision Making

The healthcare industry is in the midst of a transformation. The value of data generated by sensors will be huge, and that’s why healthcare organisations are investing in IoT and AI services to help them gain insight into patient health and wellness, improve care delivery and save money.
IoT is expected to have the largest impact on digital health as a result of its ability to track physical activity, sleep patterns and other vital signs that can help predict disease risk and treatment outcomes.
IoT offers hospitals and other healthcare providers an opportunity to improve efficiency and safety. At the same time, reducing costs through better patient satisfaction scores — but also poses some unique challenges for IT leaders at these organisations, who must balance security requirements with an overall desire for greater transparency in the day-to-day operations of their hospitals or clinics.

1. Heart Rate Variability (HRV)

Heart rate variability is a measure of how often your heart beats and how slowly and quickly it beats. It is one of the best predictors of future cardiovascular events, such as heart attack, stroke or death from heart disease.

2. Blood Pressure

Blood pressure is a measurement for the pressure exerted by blood against the walls of arteries. If you have high blood pressure, it means that your body has too much resistance to blood flow, putting more strain on your heart and other important organs. High blood pressure can lead to serious health problems such as heart disease, kidney failure and stroke.

3. Sleep Quality

Sleep quality is measured by the amount of time spent in stages 1-3 sleep (deep sleep). When you are asleep, your brain and body are at rest while still allowing you to dream and process information from the day before, which helps us to prepare for tomorrow’s activities, so we don’t get behind schedule! Poor quality sleep can cause fatigue during waking hours, leading to poor performance at work or school due to a lack of proper rest.

4. Patient Behavior Data (PB)

Patient behaviour data refers to information that is collected by sensors and other devices while a patient is using a device or interacting with a system. This includes demographic information such as age, gender, and race or ethnicity; behavioural information such as movements or activities; physiological information such as vital signs (temperature, heart rate, blood pressure, etc.). This type of data helps healthcare providers determine how to best improve their products and services for patients. For example, suppose patients experience pain during certain activities or areas of the body. In that case, you might want to adjust the settings on your product to accommodate that particular situation.


The sensor-generated data in healthcare is a collection of information that is derived from medical devices and other medical equipment. This data can transform healthcare services by providing specialists with automated strategies to measure vital signs and other variables.
The information collected by sensors can be used to help healthcare professionals make more informed decisions about their patient's health conditions and treatment options.

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