The Internet of Things (IoT) refers to the growing number of internet-connected devices that can collect, analyze, and share data. This technology is rapidly transforming many industries, and healthcare is no exception. IoT has the power to revolutionize patient monitoring, care delivery, medical research, and more.
What is IoT in Healthcare?
IoT in healthcare involves connecting medical devices and healthcare systems using embedded sensors and connectivity. This allows real-time data collection, analysis, and even automated actions.
Some examples of IoT devices used in healthcare settings include:
- Wearable devices like smartwatches that track vitals
- Sensors that monitor air quality, hand hygiene, room occupancy
- Telehealth devices for remote patient monitoring
- Smart beds that detect patient movement
- Asset trackers for equipment and medication
- Smart inhalers and pill bottles that track usage
All this IoT-enabled equipment connects back to healthcare IT systems, providing a wealth of data.
According to a survey by Business Insider Intelligence, 27% of healthcare organizations are currently using connected IoT technology for a range of use cases. With that number expected to grow to 85% by 2025 based on forecasts, it shows the rapid pace of adoption underway.
Types of Data Generated from IoT Devices
Each IoT device captures different signals and data attributes, for example:
| IoT Device | Data Attributes Captured |
|---|---|
| Cardiac monitor | Heart rate, blood pressure, ECG readings, oxygen saturation |
| Smartwatch | Steps, heart rate, sleep patterns |
| Room sensors | Humidity, air particles, occupancy, equipment usage |
| Smart beds | Patient movement, duration in bed |
| Medication trackers | Prescription details, pill counts, refills |
By aggregating and analyzing data from these disparate devices, healthcare providers gain a 360-degree patient view. This is where machine learning and big data analytics come into play.
Harnessing Healthcare Data with Machine Learning
With Internet of Things, data is constantly flowing in from medical devices across the healthcare network. This buildup of mammoth datasets calls for automated approaches to garner meaningful signals and insights. This is where machine learning and artificial intelligence can provide immense value.
Machine learning examines millions of IoT data points to uncover patterns that would remain hidden to human analysts. These could be patterns indicating disease prognosis, likelihood of hospital readmission, best medication course based on biomarkers, and more. Models get more intelligent over time as they ingest more training data.
Some promising use cases where IoT data combined with machine learning can transform decision making include:
1. Predictive monitoring: Systems ingest physiological data from cardiac monitors, glucose meters and analyze it to forecast adverse events so providers can proactively intervene.
2. Clinical decision support: Doctors input patient symptoms and vitals into ML systems which output possible diagnoses along with confidence scores to assist human experts.
3. Inventory optimization: Algorithms analyze equipment usage sensors and trends to optimize device reorders and predict device failures before they impact patient care.
4. Clinical trial recruitment: Based on IoT devices monitoring patient attributes, ML matches eligible candidates to experimental drug trials they may benefit from.
The capabilities of machine learning models grow exponentially more powerful as they process ever more data signals through neural network architectures. This makes centralized data and interoperable systems essential to maximize value.
Architecting the IoT Healthcare Data Ecosystem
To fully benefit from machine learning advancements in medicine, healthcare systems need an integrated architecture for securely managing high volumes of real-time IoT data including:
- Data ingestion pipelines supporting variety of access protocols
- A cloud-based data lake allowing unified storage in native formats
- Cluster computing to accelerate analytics on billions of data points
- Data mapping to standard terminologies like FHIR, SNOMED
- Role-based data access controls and de-identification
- Tools for custom model development and deployment
- Resulting analytics fed back into clinical workflows
This foundation empowers big data AI while also maintaining HIPAA privacy/security compliance and clinical accuracy. With thoughtful architecture, healthcare leaders can drive a transformation towards proactive, predictive, and patient-centered medicine powered by their IoT data capital.
My perspective is that purposeful design of the machine learning stack will unlock exponentially growing value from IoT devices as models mature. Automating lower complexity analytical tasks through augments analytics also helps healthcare practitioners focus more on higher-judgement activities like diagnosis validation and care plan nuances.
Privacy & Ethical Considerations for Healthcare IoT Data
While the promise of data-driven IoT healthcare cannot be ignored, it does raise valid considerations healthcare leaders must address around privacy, consent, algorithmic bias, and responsible data usage. A few reflections include:
- How can patient consent processes transparently reflect changing future uses of rapidly accumulating data from IoT sources? What notice is owed?
- As decision-support algorithms ingest observational health data, what controls prevent encoding of socioeconomic biases or unfairness? How do we monitor model performance across subgroups?
- How do we balance reliance on data-derived probabilities and correlations vs physician diagnosis through traditional techniques? What overrides or appeals should exist in automated decision loops?
I expect robust public discourse on these aspects to continue, and likely spawn regulatory proposals. Through proactive ethics review boards, external algorithmic audits, and patient inclusion – healthcare systems can uphold public trust while still realizing breakthrough potential from health-IoT data. But it will require diligent governance and leadership commitment to responsible innovation.
Current Adoption Rates of IoT in Healthcare
IoT-enabled devices have seen strong uptake by patients for managing chronic conditions. A 2020 survey by Parks Associates indicates:
- 71% of U.S broadband patients used a connected health device
- This is forecast to reach 125M patients by 2024
However, adoption by healthcare delivery businesses is still limited to an estimated 27% per previously cited research – though investment is rising.
By 2025, the global IoT healthcare market is projected to reach $344.1 billion according to a recent Fortune Business Insights report. Much of this growth aims to capitalize on opportunities like:
- Enhanced patient monitoring
- Increased medical device integration
- Clinical analytics and tracking
- Telemedicine and connected imaging
- Automation of workflows
Realizing these goals needs overcoming barriers healthcare administrators cite around justifying costs/ROI of new equipment, solving interoperability with existing systems, and having talent to manage additional infrastructure and security requirements that accompany more connected technologies.
Real-World IoT Healthcare Applications
Many leading hospitals and healthcare networks have already implemented IoT solutions and achieved tangible improvements:
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Remote cardiac monitoring allowed one California hospital to detect 90% more arrhythmias while also reducing ER visits by 87%. This resulted from early interventions guided by machine learning based risk alerts.
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An asset management system at a 500-bed Thai hospital increased usable bed count by 20% and made equipment 30% more available by reducing search time through real-time locators. This improved ability to immediately serve patients.
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Smart hand hygiene monitoring systems used at several US hospitals have improved compliance rates to near 90% according to internal audits. This is critical to reducing healthcare associated infections which affect 1 in 25 hospital patients according to CDC estimates.
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After implementing an IoT patient monitoring platform, a Boston hospital reported 98% fewer deaths from preventable pulmonary embolisms. Detection involves aggregating respiratory rate data from wearables and triggering risk alerts.
These examples exhibit how IoT data combined with proactive actions drives better efficiency, lower risks, and improved care outcomes.
The Future of IoT in Healthcare
Looking ahead, experts predict IoT will enable several revolutionary advances in medicine:
- AI-assisted diagnosis from evaluation of comprehensive patient datasets using deep neural networks capable of recognizing hard to discern patterns. This can uncover hidden root cause issues missed by traditional methods.
- Genome-specific preventative care and treatments derived by algorithms that can correlate entire genetic makeup with projected health trajectories and associated interventions tailored to one‘s unique makeup.
- Remote robotic surgery performed by top specialists worldwide by relaying near real-time imaging and sensor feeds to manipulate instruments with extreme precision.
- Early disease outbreak prediction/tracking by ingesting wearable monitors data from wider populations through public health monitoring systems and identifying spikes or clusters foreshadowing contagion spread.
- Fully automated smart hospitals with inventory robots triggering auto-reorder and autonomous drones delivering items across wards based on asset tracking telemetry.
I expect machine learning capabilities fueled by healthcare IoT data to steadily move up the capability curve. Eventually, more complex diagnoses and treatment selections may become largely automated – with human practitioners focusing more on relationship-building, care plan personalization and managing anomalies. However, humans must remain guardrails for recommendation systems as complete reliance on data & algorithms risks losing empathy and the intrinsic unstructured complexity of medicine.
As policy makers consider regulations for such a data-centric future, their perspective must accommodate the rapid pace of healthcare‘s digitization. Rules should promote access, sharing, transparency and removal of information blocking practices to fuel public good.
Overcoming Challenges with IoT Adoption
To fully leverage IoT’s advantages, hospitals need to plan appropriately for key adoption challenges:
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Upfront costs of new equipment, network upgrades, and system integration. Leadership should map out required scale of transformation and build willful capital allocation roadmap.
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Ongoing cybersecurity needs as increased attack surface from more devices poses vulnerability. Security by design principles must be followed, leveraging technologies like blockchain for integrity and access control.
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Interoperability issues with fragmented IoT solutions. This reinforces need for disciplined architecture of data flows and integration touchpoints early on.
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Compliance considerations regarding evolving patient health data policies. Keep current on latest guidance and recognize data stewardship as a core cross-functional competency, not just an IT obligation.
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Clinician training/retraining to adapt workflows, avoid alert fatigue as data scales up, and nurture confidence in algorithm aids. User centric design and change management is vital to adoption.
With careful change management IoT can provide phenomenal improvements in efficiency, costs, and care quality. But hospitals must take a strategic approach to transformational governance during implementation.
Some advice I would offer healthcare leaders pursuing IoT includes:
- Which specific pain points or constraints does IoT alleviate for your organization in order to quantify ROI?
- How will you support clinicians during adaptation to exponentially more real-time data?
- What policy gaps exist in managing new data types or usage scenarios that need addressing?
- How to architect infrastructure so it scales securely alongside projected growth?
These are some of the foundational considerations hospital executives pursuing IoT investments must reflect on to ensure success.
Conclusion: IoT as a Competitive Advantage
In closing, IoT represents a must-have set of technologies for healthcare providers to remain competitive. The level of real-time patient insights and operational control possible with IoT cannot be matched by traditional healthcare technology. IoT in healthcare has already begun saving lives and costs. Over the next decade it promises to transform medicine through substantially more data-driven diagnosis, treatment, automation and disease management. Healthcare systems that strategically adopt IoT will be best positioned to deliver superior, affordable patient care while capturing digital market share. Those who lag risk declining relevance. By taking an informed strategic approach, factoring in both future technological potential and pragmatics of change management, hospitals can fuel incredible progress.
