Healthcare data has been historically managed in a vastly different manner from what we see today. Patient information was primarily confined to paper records, hampering accessibility, interoperability, and efficient data analysis. Siloed data storage, manual data entry, and a lack of advanced analytics tools were common challenges faced by healthcare organizations. Surprising from the modern context, most hospitals in developing and under-developed nations still didn't have a solid EHR or HIMS that made matters worse. Data security concerns loomed large, and remote patient care options were limited.

Cloud transformation in healthcare is hence of no surprise. The fact that the cloud market for the healthcare industry is expanding by 20% each year, only emphasizes the importance and increasing adaptability of the cloud in healthcare.

That Was Then, This Is NOW

The healthcare sector is having its watershed moment — ready to reimagine how providers and patients deliver and experience care. In many ways, the COVID-19 pandemic exposed underlying vulnerabilities that already existed (site of care shifts, remote health, resource pressures, cybersecurity, to name a few) and set the stage for new technologies to emerge. The importance of digital healthcare is no longer debatable; what matters is the efficient use of technology such as the cloud, which provides data storage, processing, and analytical management capabilities. As we see healthcare evolve, it becomes evident that cloud-native data modernization and analytics tools are at the forefront of this shift.

Since timely intervention is a critical aspect of healthcare analytics, near real-time analysis is imperative to address alarming tendencies. Making the capacity to examine real-time data to inform decisions and interventions a huge driver in cloud analytics. In this blog, we will discuss cloud analytics for healthcare and explore several use cases that demonstrate the capabilities and possibilities for better patient care. Let us dive in.

How Cloud-Native Analytics Fits in Patient Care

The modern industry revolves around cloud and advanced technology foundations. The COVID-19 pandemic also allowed for the digitalization of two years' worth of digitization in just two months, which was also greatly achievable because of cloud capabilities, one of which being cloud analytics.

The healthcare industry has long struggled to manage and leverage the data produced daily. The traditional infrastructure or on-prem landscapes only allows healthcare industries to store the data but failed to offer timely and meaningful utilization of it. This stressed the need to harness the possibilities of cloud-native analytics. By concentrating on this key transition, a better understanding can be gathered from all that healthcare data, emphasizing the positive influence cloud analytics has on patient outcomes. Let's see how things are going so far -

Real-Life Use Cases: Cloud-Native Analytics in Patient Care Transformation

Use Case 1: Predictive Analytics for Patient Care

The medical sector generates a massive quantity of data but struggles to turn that data into actionable insights to enhance patient outcomes. Predictive analytics in the cloud enables healthcare practitioners to forecast patient outcomes and optimize care methods by examining past patient data such as vital signs, test findings, and treatment plans. For instance, it can identify patients with cardiovascular disease with the highest probability of hospitalization based on age-coexisting chronic illnesses and medication adherence.

Use Case 2: Remote Patient Monitoring

Remote patient monitoring is an important part of modern healthcare since it allows patients to get care from the comfort of their own homes. The collecting and analysis of patient data from multiple devices and sensors, including wearables and IoT devices, is facilitated by cloud-native analytics. IoT-connected medical devices, such as monitoring devices for glucose levels, heart rate, and Parkinson's, have revolutionized personal medical care. These devices can monitor patients' health 24/7 and respond instantly to any problem. If someone needs an ambulance, these technologies will be able to respond faster and more efficiently than any person. How do these devices accomplish this? They are continually transferring the patient's data to the cloud, where it is saved and linked to the appropriate medical personnel.

For example, consider a three-dimensional approach to using HMS, which includes the monitoring locations (home, remote monitoring, and clinical), the type of monitored subject (healthy and patient), and how data are processed (online and offline).

Use Case 3: Telemedicine and Virtual Consultations

The global pandemic resulted in explosive growth in the market for telemedicine solutions, bridging the gap between physicians and patients and dramatically enhancing care for people living in remote areas. Patients can now communicate with healthcare specialists from any location, ensuring access to quality care. Cloud-native analytics allows for the seamless integration of patient data, electronic health records, and video conferencing systems, resulting in an all-encompassing virtual healthcare cloud experience. Here, the managed cloud services ensure the security and compliance of patient data in telemedicine applications.

Use Case 4: Personalized Treatment Plans

Pathology, proteomics, and genomes, which are critical to customized therapy, can alone create more than 2TB of data for each patient. Personalized treatment plans are one of the most promising applications of cloud-native analytics. Healthcare providers can design individualized treatment strategies by examining a patient's medical history, genetic data, and lifestyle information. A cancer patient's treatment strategy, for example, can be tailored to their genetic profile, boosting the likelihood of successful therapy while limiting side effects. This level of customization improves the patient's experience by providing more effective and less invasive therapies.

Use Case 5: Efficient Resource Allocation

Cloud-native analytics can optimize the allocation of healthcare resources, ensuring that patients receive timely care. By analyzing data on patient admissions, staff availability, and equipment usage, hospitals can make informed decisions about staffing levels, bed allocation, and supply management. This leads to shorter wait times, reduced overcrowding, and a smoother patient experience.

Use Case 6: Healthcare Data Sharing and Interoperability

A surge in the number of healthcare devices, particularly wearables, has resulted in new sources of personal telemetry data. Integrating all this data, as well as facilitating data interoperability among healthcare systems, can have significant advantages for both clinicians and patients. For example, healthcare practitioners can improve outcomes and save costs to build a single, comprehensive perspective of each patient.

Cloud analytics also offers a modern option for gathering, analyzing, displaying, storing, and safeguarding data in the healthcare industry, such as constructing data pools and warehouses on the cloud. It eliminates silos between applications and departments and enhances the performance of important apps. With data no longer fragmented or kept in department-specific silos, medical professionals can work with the most up-to-date and comprehensive information.

Additionally, since AI and machine learning algorithms thrive on huge and diverse datasets, and cloud- based data pools provide the resources these technologies require to discover trends, make forecasts, and support healthcare professionals

Use Case 7: Reinforced Cybersecurity

Cybersecurity attacks on healthcare organizations accounted for 79 percent of all attacks recently. Cloud-native analytics can improve data security by providing secure storage, analysis, and access to patient data, ensuring security from cyber threats. It is imperative that healthcare cloud solutions adhere to the latest security guidelines, and that they are fully compliant with HIPAA, FDA, and HITRUST standards.

In addition, advanced cloud security tools like Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) solutions improve security. These solutions continuously ingest and analyze IT telemetry 24/7, allowing them to predict lingering threats or security flaws.

Use Case 8: Quality Improvement and Patient Feedback Analysis

Patient feedback is invaluable for improving the quality of care. Cloud-native analytics can be used to collect and analyze patient surveys, reviews, and comments to identify areas for improvement. By actively listening to patient voices, healthcare providers can make necessary adjustments to enhance the patient experience.

Use Case 9: Real-time Monitoring of ICU Patients

In intensive care units (ICUs), cloud-native analytics systems can continuously collect data from various monitoring devices like ECG machines, ventilators, and vital sign monitors. By analyzing this data in real-time, healthcare providers can detect anomalies, predict deteriorations, and trigger immediate interventions. For instance, a study published in the "Journal of Critical Care" described how a cloud-native analytics platform identified early signs of sepsis in ICU patients, allowing medical staff to intervene promptly and improve patient outcomes.

Use Case 10: Genomic Data Analysis for Precision Medicine

A single human genome sequence generates over 200 terabytes of raw data. If we sequence 100 million genomes by 2025, we will have amassed more than 20 billion terabytes of raw data. Such a massive amount of data needs managing. Cloud-native analytics can process and analyze vast amounts of genomic data to support precision medicine. For instance, analytics tools may be able to identify specific genetic mutations in cancer patients. These findings can then further help in the development of personalized treatment plans, selecting meds that target the patient's unique genetic makeup, thereby increasing the likelihood of successful treatment and minimizing side effects.

Use Case 11: Maintenance for Medical Devices

Cloud-native analytics isn't limited to just patient care but also extends to the maintenance of medical equipment. Cloud analytics can be used to predict maintenance needs for these devices. By continuously monitoring data and analyzing patterns, healthcare providers can predict when a device might fail and schedule maintenance before it happens. Example, the analytics system may help reduce downtime of MRI machines in a hospital by predicting and preventing mechanical failures. By analyzing usage data and performance metrics, healthcare providers can reduce equipment downtime, ensuring reliable service for patients.

Use Case 12: Medication Adherence

Cloud-native analytics is crucial in improving medication adherence, which is a critical aspect in patient care. Cloud-native health apps encourage patients to seek care on time by sending medication reminders and notifications, tracking medication usage, creating individualized medication plans, providing patient education, and giving healthcare providers real-time data access. Patients are now empowered to take a more active role in their healthcare, which leads to better treatment outcomes and general well-being. The Apple - Health app, for example, delivers vital information at your fingertips, such as your health data, prescriptions, lab results, activity, and sleep. It also makes it easier to share such information securely.

Use Case 13: Radiology Image Analysis

Cloud-native analytics is increasingly used in radiology to enhance the accuracy and efficiency of image analysis. It can provide real-time data analysis and actionable insights; due to the deep learning algorithms hosted on the cloud to improve the detection of abnormalities in medical images. The cloud infrastructure can allow radiologists to access and process images from anywhere, facilitating faster diagnoses and reducing the patient's time to treatment.

Use Case 14: Predicting Disease Outbreaks

Now that medical records are being stored digitally, rather than on paper, data can be simply anonymized and then analyzed. Cloud-native analytics can assist in predicting such outbreaks and epidemics by analyzing vast datasets, including patient health records, epidemiological trends to assess the current public health threats and environmental factors, preparing for them more effectively. It can also help in estimating the need for beds and predicting care needs following an outbreak. For instance, a cloud-based predictive model can monitor symptoms reported by patients through mobile applications. These early warning signs may help healthcare organization experts, tracking such data to recognize trends and take appropriate measures before we face another global pandemic.

Use Case 15: Clinical Trial Optimization

Cloud-native analytics plays a crucial role in optimizing clinical trials by analyzing patient data and trial results from multiple sites enrolled, thousands or even tens of thousands of remotely monitored participants, and other large, random studies. By examining data from previous trials, researchers can identify trends, patient subgroups that respond well to treatments, and potential risks. This information is used to design more efficient and patient-centric trials, ultimately accelerating the development of new therapies.

Use Case 16: Early Detection of Patient Deterioration

Cloud-native analytics can continuously monitor a patient's electronic health records (EHR) and vital signs data. The data is collected from the systems and stored on the healthcare cloud, empowering organizations to perform analytics and create better solutions. By applying advanced algorithms, it can detect subtle changes in the patient's condition that may signal deterioration. This early detection allows healthcare providers to intervene before a critical situation develops, significantly improving patient outcomes.

Use Case 17: Predictive Staffing and Resource Allocation

Hospitals often face challenges in managing staff levels efficiently. By analyzing historical patient admission patterns, patient acuity, and seasonal factors, hospitals can forecast staffing requirements accurately. Cloud services then ensure the right number of healthcare professionals are available, and then some to meet patient needs and optimize resource allocation.

Use Case 18: Drug Discovery and Research

The initial stage of the drug development process is drug discovery, in which researchers uncover new chemicals that can potentially cure a targeted disease. It entails filtering hundreds of thousands, if not millions, of active molecules as well as multiple testing. Cloud analytics provides big data analytics and AI capabilities that enable researchers in the healthcare sector to navigate through loads of data points by streamlining and enabling efficient data management, analysis, and modeling. Cloud-based platforms may also allow researchers to identify potential drug targets, design new compounds, and predict their effects more quickly and accurately.

Use Case 19: Anomaly Detection in Medical Billing

Cloud analytics can help with anomaly detection in medical billing by leveraging sophisticated machine learning algorithms to flag unusual billing patterns, anomalous spending and root causes. Cloud analytics continuously analyzes billing data, alerting on abnormal cost spikes without custom configurations so healthcare providers can quickly take action. It can also help identify anomalies in a multivariate dataset by taking advantage of the interrelationship among signals, maximizing the accuracy of identified anomalies.

Use Case 20: Emergency Room Triage Optimization

Overcrowding and extended wait times are common in emergency rooms. Cloud-native analytics can help optimize patient triage in real-time by analyzing patient data and the severity of their conditions, the system assigns priorities, ensuring that critical patients receive immediate care while streamlining the process for the lesser severity cases. It also enables emergency departments to ensure they have the staff they need on hand at all times.

Use Case 21: Patient Pathway Optimization

Cloud-native analytics can improve patient pathway optimization by analyzing patient journeys through healthcare systems, throughout the entire patient journey. Cloud analytic tools can be used to optimize the patient flow within a hospital reducing waiting times, faster diagnoses, and smoother overall patient experience. For example, a study published in the "Journal of Medical Internet Research" demonstrated how cloud-native analytics can continuously monitor a patient's electronic health records (EHR) and vital signs data, providing real-time data analysis and actionable insights, enabling healthcare providers to make more informed decisions about patient care.

Use Case 22: Population Health Management

Population health management involves analyzing data from a large group of patients to identify trends and risk factors. Cloud-native analytics can help healthcare systems proactively manage the health of their populations by monitoring chronic conditions, vaccination rates, and social determinants of health. This enables health providers to create targeted interventions and prevention strategies to improve the overall health of a community.

Use Case 23: Fraud Detection and Prevention

Cloud services are also critical in preventing fraud among patients and healthcare institutions. Anomalies and irregularities can be found by evaluating billing and claims data in real time and employing machine learning algorithms and statistical models to identify suspected fraudulent actions. This helps to prevent fraudulent activities like inappropriate payments, duplicate claims, odd billing spikes, or suspicious provider conduct, which can hurt both patients and the healthcare system. Eventually, contributing to a more safer and trustworthy patient experience.

Use Case 24: Cloud-based AI-driven Video Analytics

Cloud-based AI-driven video analytics is a subset of cloud-based data analytics that focuses on storing and analyzing video data using artificial intelligence and machine learning techniques. It is an emerging technology, with a potential to improve training, governance, and standardization procedures. Moreover, with the future adaptation of the technology, CAVs can be integrated into the trainees' portfolios as part of their virtual curriculum.

Use Case 25: Healthcare Marketing

Cloud-native analytics are used in healthcare marketing to improve patient outreach, personalized campaigns, and data-driven engagement tactics. Cloud-native analytics can segment patient populations into distinct categories depending on their healthcare needs and preferences by evaluating patient data such as electronic health records and demographic information. This segmentation enables targeted marketing efforts that provide patients with suitable and up-to-date information on preventative care, health tests, or services that are relevant to their individual health goals.

Cloud-native Analytic Tools Across Different Hyperscalers

81 percent of health sector executives are reporting that their cloud transformations are now underway, according to PwC's 2023 Business Cloud survey. Here's how it's being used:

  • Google Cloud: Google Cloud services offers a variety of data management, machine learning, and artificial intelligence (AI) services, all in a single end-to-end platform. These services facilitate the creation, ingestion, and search of clinical and medical imaging data, while other technologies can assist clinicians in managing and safeguarding patient data. For instance, HCA Healthcare is tapping into Google's data science expertise to develop new analytics capabilities around patient care and administrative workflows. HCA hopes to deliver algorithm-based alerts to clinical staff's mobile devices when a patient's condition starts to change.
  • AWS: AWS also provides cloud-native analytics tools that provide secure storage and access to patient data, enabling healthcare providers to make more informed decisions about patient care. For instance, Pfizer, the firm developing the famed COVID-19 vaccine, has begun working alongside Amazon Web Services (AWS) to create cloud-based solutions that aim to accelerate and improve the development, manufacturing and distribution processes for clinical trial testing.
  • Microsoft Azure: Microsoft is known for creating a myriad of software applications, several of which can and are being utilized by the healthcare industry. Used in conjunction with AI tools and IoT technology, Azure cloud can also be used for monitoring patient insights and analytics. Microsoft Azure provides cloud-native analytics tools such as cloud-based electronic health record (EHR) platforms and cloud-based telehealth and virtual care platforms to provide secure storage and access to patient data.

Cloud4C: Connecting Hospitals, Patients, and Data

According to McKinsey, the cloud has the strength to generate a whooping value of $100 -170 billion for healthcare organizations by 2030 and cloud's analytical capabilities are a big reason for it. As technology continues to advance, we can expect even more innovative applications of cloud-native analytics in healthcare, further improving the patient experience. Cloud4C can be a great contributor to this.

Cloud4C is one of the largest managed cloud services providers, offering multi-cloud migration and AIOps-driven managed services with a zero data loss guarantee, supporting cloud environments, all under a single SLA. Our Healthcare-in-a-Box solution is an end-to-end, packaged cloud healthcare suite, designed to address the three main roadblocks to unlocking the next generation of healthcare. The suite includes personalized, patient-first and data-envisioned healthcare at zero disruption, continuous patient-monitoring, seamless data interoperability, operational analytics, and so much more.

Contact us to know more on how we can help you explore the full potential of cloud-native analytics in the digital healthcare space.

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Harsha Vardhan Sidda

Leading Analytics and AI practice at Cloud4C, Harsha has spearheaded successful data transformation projects for leading BFSI, healthcare, logistics, and manufacturing companies, helping the latter firms maximize their data potential with utmost returns.

author img logo
Dr. Anil M

Dr. Anil heads healthcare transformation vertical at Cloud4C. A vocal advocate of evolving healthcare operations with cutting-edge technologies such as AI, IoT, Big Data, Cloud, he envisions a digitally-driven inclusive healthcare for all.

author img logo
Harsha Vardhan Sidda

Leading Analytics and AI practice at Cloud4C, Harsha has spearheaded successful data transformation projects for leading BFSI, healthcare, logistics, and manufacturing companies, helping the latter firms maximize their data potential with utmost returns.

author img logo
Dr. Anil M

Dr. Anil heads healthcare transformation vertical at Cloud4C. A vocal advocate of evolving healthcare operations with cutting-edge technologies such as AI, IoT, Big Data, Cloud, he envisions a digitally-driven inclusive healthcare for all.

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