WHITE PAPERHARNESSINGTECHNOLOGY FOR BETTERHEALTH OUTCOMES
Early Detection And Real-Time Alerting Through IoT and AI

RAMESH RADHAKRISHNANGOPAKUMAR NSUSANNA MARY BABUTABLE OF CONTENTS
Executive Summary

3Conclusion

8Introduction

4

References

9

Real-time Alerting andEarly detection &reporting of symptoms

5

Enhancing Early Warning
Systems withTechnology

6Challenges

8

EXECUTIVE SUMMARY

Early diagnosis of preventable diseases can be lifealtering. Detecting early onset symptoms ofconditions like Alzheimer's, Parkinson's, Cancer,etc. can help mitigate serious health risks.
Disruptive technological advancements like theInternet of Things (IoT) and Artificial Intelligence(AI), specifically Machine Learning (ML) and Deep
Learning (DL) models can notify users of earlysigns of diseases. This empowers them to get
medical attention preemptively and addressconditions promptly before they aggravate.

XPage 3

With this white paper, we aim to address the current
scenario of technology and software solutions andtheir role in early detection, alerting, and preventionof diseases. Furthermore, this study criticallyexamines challenges like data security and privacy
concerns inherent in utilizing software solutions
and algorithms. By exploring the ethical andregulatory
implications
ofutilizing
suchtechnologies in healthcare settings, we aspire tofoster a nuanced understanding of the challenges
and opportunities associated with integrating
disruptive technologies into disease prevention
efforts.

Harnessing Technology for Better Health OutcomesINTRODUCTION
With advancements and improvements intechnology, the healthcare sector's technology
adoption rate has been skyrocketing.
Technology integration in healthcare
encompasses a range of tools, includingdiagnostic imaging, AI-enhanced live-view
monitors for guided surgeries, and real-timemonitoring and alerting devices. It also
includes patient care and data management
platforms. These technologies have proven to
be valuable for individuals, healthcare
institutions, and hospitals alike. In recent years,
there has also been a hike in the number ofhealthcare applications designed specificallyfor mobile devices and everyday wearable
gadgets like smartwatches and smartbands.

Market Growth of global m-health Apps
Market Growth$ 113.2 B

CAGR = 14.9%(from 2024 to 2031)$ 37.25 B

2023 2024 2025 2026 2027 2028 2029 2030 2031

The World Health Organization (WHO) reportsthat millions of preventable deaths occur
each year due to late detection. Studies haveshown that early detection of life-threatening
diseases like cancer can significantly improvesurvival rates. Unfortunately, approximately
50% of cancer is diagnosed at an advanced
stage[1] thereby significantly cutting down on
chances of survival. This could be avoided byearly detection and recording of symptoms
aiding the physician, in arriving at an accurate
and timely diagnosis.

Page 4Harnessing Technology for Better Health OutcomesREAL-TIME ALERTING AND
EARLY DETECTION &REPORTING OF SYMPTOMS
An early detection and recording of symptoms is crucial for timely diagnosis andintervention. From the admission of a patient, data should be continuously collectedand recorded in the patient’s EMR (electronic medical record). A patient’s EMR
includes the patient’s primary details, treatment history, vitals, medical imaging data,etc. The consulting physician is then presented with this complete set of information
enabling them to make an accurate diagnosis. This EMR data could be coupled withML models to detect symptoms and/or predict mortality risk.

Personal DataCollection

Vitals Taken

Blood & Serum
Testing

Medical Imaging &Analysis

Presently, the solution closest to an early detection system is an Early Warning System(EWS) which is an on-premises, connected system. The system can predict in-patientmortality risk by integrating the patient’s Electronic Medical Record (EMR) data. Data
including demographics, clinical utilizations, medical history, vitals, lab results, andEMR data is collected from patients within a hospital or a network of hospitals.
Collected data is utilized to assess in-patient mortality risk and corresponding risk
scores will be assigned to each patient using a combination of EMR data and Machine
Learning algorithms. When the risk score exceeds predefined thresholds, the software
sends real-time alerts to healthcare providers, allowing for immediate intervention.

Page 5Harnessing Technology for Better Health OutcomesSuch real-time alerting systems could help in timely intervention by medical professionals
thereby mitigating considerable amounts of risk. However, it is significantly limited as it isan on-premise system. It is necessary to employ a fully remote system that supports realtime monitoring of patients. Such a practice is termed Remote Health Monitoring (RHM).
In RHM various physiological data, such as blood oxygen level, heart rate, bodytemperature, and ECG signals, etc. are collected remotely[2]. These data points could beanalyzed to identify potential health issues and provide real-time feedback to healthcare
providers.

ENHANCING EARLYWARNING SYSTEMS WITHTECHNOLOGYWith advancements in technology and the eruption of disruptive technologies like AI andIoT, real-time monitoring can now be facilitated remotely enabling early detection ofdiseases. Wearable devices, cloud integration, and AI/ML data analysis models provide
real-time alerts and facilitate the early detection of diseases.

Page 6Harnessing Technology for Better Health OutcomesCollection and Storage of Data: Real-time data on vitals like heart rate, blood sugar levels,
blood pressure, and body temperature are continuously collected by wearable devices.
These real-time data streams are ingested using protocols like MQTT, HTTP/HTTPS, andintegrated into a centralized cloud platform using a gateway device. Data Lake is used asa central repository to store accounts of historical data and relational & NoSQL
databases are utilized for ensuring quick access and retrieval of structured andunstructured data.
Data Processing, Analysis & AI Model Training: The incoming data is cleaned,
preprocessed, and relevant features are extracted for analysis during the model training
process. This normalized data is then used for AI/ML model training. The trained modelassists in symptom detection and risk assessment by recognizing complex patterns
within the data sets used during training. Pre-trained models can subsequently be applied
to specific treatments with improved accuracy through Transfer Learning, which finetunes the model for application to particular classes of diseases.
Integration with EMR and Real-time Alerting: Users and their concerned healthcare
providers are notified of the detected anomalies and early symptoms. Notifications aresent via SMS, email, and push notifications. This preemptive approach can lead to timely
intervention, early diagnosis, and disease prevention. The data is seamlessly integrated
with existing Electronic Health Records (EHR) or Electronic Medical Reports (EMR) forfuture reference and a comprehensive view of the patient's medical history. To guarantee
the security and confidentiality of sensitive medical data, end-to-end data encryption androle-based access control are ascertained. HIPAA, GDPR, and other healthcare regulatory
standards are followed to ensure patient data.

Page 7Harnessing Technology for Better Health OutcomesCHALLENGES
The growing adoption of technology in healthcare also presents its own unique set ofchallenges. The early detection system relies heavily on patient data available from thepatient’s EMR and data sets used to train the algorithm. Data privacy, patient anonymity,information security, etc., should be prioritized while utilizing Artificial Intelligence andMachine Learning models. Another significant challenge is the incorrect detection ofsymptoms, false and frequent alerts, biases in the selection of training data sets, etc.

Information Security & Data Privacy: While using historical treatment data sets totrain AI it is important to prioritize patient privacy and anonymity. Hospitals andhealthcare institutions often face data security threats like hacking, malware,ransomware, etc.[3] Data encryption and access control should be practiced tomaintain information security.
Frequent Alerts, False Alerts, and Alert Fatigue: It was noted from theimplementation of CDSS that frequent alerts often lead to dismissal or inaction.
Studies estimated that 90% of drug-related alerts are overridden by prescribing
physicians,[4] and more than half of alert overrides were due to alerts being deemed
irrelevant.[5]Algorithm bias and training data set: The data set used in training AI and ML
algorithms could be irrelevant or different from the actual population on which they’ll
be giving recommendations. This data drift could be duly addressed by ensuring thedata sets diversity and inclusivity. In 2021, the WHO released its first guidance on theethics and governance of AI in healthcare[6] which indicates that AI systems should
be designed to reflect the diversity of socioeconomic and healthcare settings[7].

CONCLUSION
We’ve discussed how early detection of symptoms and real-time alerting help in reducing
mortality risk and improving the quality of life. The recent healthcare trends show how
Artificial Intelligence has been pivotal in improving the healthcare system. Software
solutions leveraging machine learning and deep learning algorithms are at the forefront of
this improvement. We have also explored the challenges that arise with the use of such
solutions primary of which is the data privacy and security concerns. By staying compliant
with regulatory standards like HIPAA and HL7 the discussed challenges could be cut down.
Technology advancements can thus improve healthcare service delivery significantly
without compromising data privacy.

Page 8Harnessing Technology for Better Health Outcomes

REFERENCES[1] David Crosby et al. , Early detection of cancer, Science375, eaay9040(2022),
DOI:10.1126/science.aay9040[2] Islam, M.R.; Kabir, M.M.; Mridha, M.F.; Alfarhood, S.; Safran, M.; Che, D. Deep
Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues
in Real-Time. Sensors 2023, 23, 5204. https://doi.org/10.3390/s23115204[3] Abouelmehdi, K., Beni-Hessane, A. & Khaloufi, H. Big healthcare data: preserving
security and privacy. J Big Data 5, 1 (2018). https://doi.org/10.1186/s40537-017-0110-7[4] Isaac T, Weissman JS, Davis RB, et al. Overrides of Medication Alerts in Ambulatory
Care. Arch Intern Med. 2009;169(3):305–311. doi:10.1001/archinternmed.2008.551[5] Maxim Topaz, Diane L Seger, Sarah P Slight, Foster Goss, Kenneth Lai, Paige G
Wickner, Kimberly Blumenthal, Neil Dhopeshwarkar, Frank Chang, David W Bates, LiZhou, Rising drug allergy alert overrides in electronic health records: an observational
retrospective study of a decade of experience, Journal of the American Medical
Informatics Association, Volume 23, Issue 3, May 2016, Pages 601–608,
https://doi.org/10.1093/jamia/ocv143[6] WHO Guidance Paper - Ethics and Governance of Artificial Intelligence for health:
https://iris.who.int/bitstream/handle/10665/341996/9789240029200-eng.pdf[7] Nazer LH, Zatarah R, Waldrip S, Ke JXC, Moukheiber M, Khanna AK, Hicklen RS,
Moukheiber L, Moukheiber D, Ma H, Mathur P. Bias in artificial intelligence algorithms
and recommendations for mitigation. PLOS Digit Health. 2023 Jun 22;2(6):e0000278.
doi: 10.1371/journal.pdig.0000278. PMID: 37347721; PMCID: PMC10287014.

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