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Health IT Revolutionizes Dialysis

Emerging technologies derived from AI, ML, and robotics are increasingly transforming medical procedures and devices and will offer great opportunities for dialysis therapy in the future.

Renal transplantation is the treatment of choice for chronic kidney disease (CKD) patients, but the shortage of kidneys and the disabling medical conditions these patients suffer from make dialysis essential for most of them. However, the process takes a long time to complete and is costly, not just for the patient but for insurance companies and facilities. Currently, the technical limitations of the equipment for supporting continuous renal replacement therapy (CRRT) include the software for hemodynamic optimization, the need for a continuous evaluation of the efficiency of solute and fluid removal, and the management of big amounts of data. But nowadays there has been research conducted into improving dialysis technology. A desired technical improvement of dynamic CRRT would be the availability of online tools for continuous, real-time data acquisition from the machine and from patients’ electronic medical records to establish an automatic biofeedback loop.

Monitoring and data collection systems based on various connectivity platforms (machine, local, or cloud-based) will help nephrologists to evaluate whether endpoints of the delivered treatment are achieved and support modifications of the initial CRRT prescription based on updated clinical targets.

Since dialysis drastically affects the patients’ lifestyle, there are great expectations for the development of wearable artificial kidneys, although their use is currently impeded by major concerns about safety. On the other hand, dialysis patients with hemodynamic instability do not usually tolerate intermittent dialysis therapy because of their inability to adapt to a changing scenario of unforeseen events. Thus, the development of novel wearable dialysis devices and the improvement of clinical tolerance will need contributions from new branches of engineering such as Artificial Intelligence (AI) and machine learning (ML) for the real-time analysis of equipment alarms, dialysis parameters, and patient-related data with a real-time feedback response. Emerging technologies derived from AI, ML, electronics, and robotics will offer great opportunities for dialysis therapy, but much innovation is needed before we achieve a smart dialysis machine able to analyze and understand changes in patient homeostasis and to respond appropriately in real-time. Great efforts are being made in the fields of tissue engineering and regenerative medicine to provide alternative cell-based approaches for the treatment of renal failure, including bioartificial renal systems and the implantation of bioengineered kidney constructs.

Recent advancements in AI technology and sensing have been used in intensive care units to predict which patients are at the highest risk of imminent deterioration and to alert staff and for monitoring critically ill patients and their environment using wearable sensors, namely, light and sound sensors and a high-resolution camera. However, AI technologies face ethical and legal challenges yet to clarify. Google DeepMind launched a pivotal project to develop a clinical alert smartphone app that functions as a data-integrating user interface to check test results for signs of acute kidney injury (AKI). The aim was to build a real-time clinical analytics, detection, and diagnosis and decision support to support treatment and avert clinical deterioration across a range of diagnoses and organ systems. However, the project was halted due to a lack of privacy and consent to transferring population-derived datasets to large private prospectors.

Government role
Providing for renal transplant facilities for end-stage renal disease (ESRD) patients depends upon the availability of infrastructure and robust organ donation system coupled with adequate availability of trained qualified manpower. Within the limited choices, dialysis practically remains the first and in the majority of cases, the only choice for ESRD patients. The kidney community is aiming to continue reducing the impact of kidney disease on patients’ day-to-day activities and quality of life. The government also has invested in improving kidney care through implementation of various programs countrywide including the Pradhan Mantri National Dialysis Program.

Every year about 2.2 lakh new patients of ESRD get added in India resulting in additional demand for 3.4 crore dialysis every year. With approximately 4950 dialysis centers, largely in the private sector in India, the demand is less than half met with existing infrastructure. Since every dialysis has an additional expenditure tag of about `2000, it results in a monthly expenditure for patients to the tune of `3-4 lakhs annually. Besides, most families have to undertake frequent trips, and often over long distances to access dialysis services incurring heavy travel costs and loss of wages for the patient and family members accompanying the patient. Keeping this in mind, strengthening of district hospitals by providing affordable multispecialty care including dialysis services in district hospitals is an important step in this direction.

To gain from available capacity of private sector existing in dialysis care segment and their capability to install and operate dialysis care system in quick time, and compliment the emerging strengths of the public sector such as availability of drugs and diagnostics, it has been proposed that dialysis program be undertaken in public-private partnership (PPP). Based on consultation with experts and discussion with some of the states implementing the dialysis program in the PPP mode, several points are considered as the ideal and cost-effective approach. For instance, it is desirable to roll out dialysis services in the states, beginning with the district hospitals in a PPP mode as direct provisioning by the state governments would be time-consuming and likely to be costly and risky; service provider should provide medical human resource, dialysis machine along with RO water plant infrastructure, dialyzer, and consumables; and the payer government should provide space in district hospitals, drugs, power, and water supply and pay for the cost of dialysis for the poor patients.

Future advances
Patients and caregivers can expect to see additional advances in the immediate future. First, a number of new hemodialysis technologies will be coming to the market. These devices have the potential to offer biofeedback loops to prevent intradialytic hypotension, increase monitoring during dialysis treatment, and transfer data between the electronic medical record and the dialysis machine, thereby reducing the data collection burden and allowing for personalized dialysis prescriptions. User-friendly hemodialysis machines are being developed to give more patients the opportunity to experience the health benefits of hemodialysis at home. Efforts also have been made to develop a system that generates sterile peritoneal dialysis solution at home, eliminating the need to transport and store large bags of fluid. All of these innovations may allow for more personalized dialysis sessions, greater flexibility, and improved quality of life for patients.

The most recent trends in the development of computer-based intelligent decision support systems (DSS) and expert systems applied to dialysis are based on artificial neural networks (ANN) and genetic algorithms that must learn their knowledge interactively from the users. However, much innovation on AI and ML is needed before one could achieve a smart dialysis machine able to analyze and understand changes in patient homeostasis and respond appropriately in real-time. The technological advancements required include machine size, network connectivity (predominantly in the form of the IoT), and computing efficiency. In addition, these resources need to be robust, fault tolerant, and cost-efficient. For this purpose, device size is still a relevant factor, because only the large ones are currently able to store the amounts of data required for continuous learning. On the other hand, miniaturized portable devices must constantly download data for DSS and expert system.

Thus, connectivity is essential for accessing large amounts of data, and small dialysis devices will require continuous and robust connectivity in order to ensure continuous patient monitoring and treatment optimization. Nevertheless, current developments in computer chip miniaturization and speed will surely facilitate the development of autonomous dialysis devices without requiring the download of data.

The challenges for future dialysis devices also include the design of smart dialysis software that automatically takes smart decisions. AI, ML, and robotics will improve dialysis therapy. Prescription of operational parameters and delivery data displays should be easily accessible via a customized easy software interface. Currently, dialysis must be operated by nephrologists to provide patients with an accurate functional program, as well as with the delivery of the scheduled therapy. Furthermore, there are still major concerns about safety, since deep learning, the currently most promising ML approach, can still be considered a kind of black box, mostly unable to explain its inner workings.

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