The case of #AI medical software regulation in developing countries by Dr. Sandeep Reddy, @docsunny50




Has the cart been placed in front of the horse? The case of AI medical software regulation in developing countries.

Medical software is defined as the use of software for medical purposes. The uptake of medical software in healthcare has increased in line with increased application computation in healthcare delivery. Examples of medical software include software used in bedside monitors, MRIs, PACs, radiation therapy software, infusion pump rate devices, smartphone-based health applications. Etc. 

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Data as an identity, diagnosis, health coach, drug and treatment by Prof. Rajendra Pratap Gupta, @rajendragupta

Electronic Health Records make data the identity for the patient. It can be in form of UHID or ADHAAR (in India). The data reveals the identity of the patient

Recently, I was in Bangladesh on the call of the Prime Minister’s office to speak on Big Data, Artificial Intelligence (AI) and to help draft the AI strategy for Bangladesh. I shared, that Big data is going to change the way we deliver healthcare, and how “big data combined with AI is not just data, but an identity, diagnosis, health coach, a drug and treatment when it comes to healthcare delivery”. It was based on the enormous possibility of what data & AI can do to healthcare delivery. 

Data as Identity: Electronic Health Records make data the identity for the patient. It can be in form of UHID or ADHAAR (in India). The data reveals the identity of the patient 

Data as diagnosis: According to Intel, AI in a single heartbeat can look at 10,000 attributes with 90 % accuracy and traditional methods look at 7 attributes with 56 % accuracy. Google’s AI algorithms help in diagnosing diabetic retinopathy. AI has been proven to be accurate in radiology for reading images and diagnosis. During the recent floods in Kerala, AI backed system, UptoDate was used by about 320 doctors in diagnosis and treatment. This solution has millions of cases in its repository and is used by clinicians worldwide, and the list goes on, on how Big Data and AI are helping in diagnosis. Big data and AI are increasingly being used as a diagnostic tool and its accuracy is of ‘clinical grade’ in specialties it has been used. 

Data as a health coach, a drug & Treatment: More than four years ago, I wrote an article on Software as a drug (SaaD) https://bit.ly/2TfWWDG 

Blue Star by Welldoc is a great example of how insulin dose can be calibrated by AI and does not need doctors. This mobile app for diabetes management is cleared by the US FDA, and it guides the patient to adjust the dosage of insulin with options of activity and diet (healthy choices with restaurant helpers), and provides with over 20,000 coaching messages and has been proven to reduce HBA1C by an average of 2 points between 3-6 months. This is considered a great achievement in the field of endocrinology. 

Need for an AI strategy: It is time that countries shape up their AI roadmap / strategy for every sector. Imagine if public hospitals in India like AIIMS, PGI Chandigarh, JIPMER, SGPGI & Tata Memorial feed in the daily OPD / IPD data and create an AI tool? How much the tool can help the ‘young medical graduates’ in accurate diagnostics and treatment? Today, timely diagnosis and treatment remains the biggest challenge in healthcare and it leads to over diagnosis and wrong treatment! With Ayushman Bharat covering over 500 million population, the data from this scheme can be used to create an AI tool in healthcare which can be shared with LMIC countries, and can serve as an important tool in form of healthcare diplomacy. Moreover, young medical graduate with such an AI tool with millions of cases in its repository will have the experience of a senior doctor with decades of experience when he or she uses an AI tool to diagnose and treat patients. Also, such a tool can prove to be a boon in rural areas as well, where people suffer the most.

In 2015, when I was writing a book on healthcare reforms (Healthcare Reforms in India – Making up for the lost decades), I did a survey with patients of RML & AIIMS and it was revealed that, patients visit an average of 6 doctors before visiting AIIMS, and add to this, the fact, that the reason for their AIIMS visit is, ‘treatment failure or not diagnosed’ from the earlier facilities or doctors visited! Moreover, it is not possible for everyone in India to reach AIIMS, Delhi or Tata Memorial in Mumbai. In such a serious situation, it makes sense for India to invest in developing an AI tool based on patient’s data in public healthcare facilities, and the patients covered under Ayushman Bharat. Also, this data can be used for fraud detection. 

AI is likely to create jobs and add to the economy. If India builds a proper AI strategy, it needs to look at the following components; Data storage, data security, data transfer, certifications and compliance, and each has the potential to add billion dollars to the economy 

Components of AI strategy: India needs a detailed roadmap for data storage, networking infrastructure, data governance, Sensors & IOT, training and human resources & research and development 

Big Data & AI hold a lot of promise for outcome driven healthcare which is accountable and affordable and India must aim to be a global leader in Big Data & AI.

(Prof. Rajendra Pratap Gupta is a leading public policy expert and is a former advisor to the Union Health Minister, Government of India ) 

The article was first published here, its republished on the HCITExperts Blog, with the author's permission

Author
Rajendra Pratap Gupta
Rajendra Pratap Gupta (Rajendra) is an original thinker and an innovator and one of the most influential and sought after public policy expert in the country. He has worked with some of the largest organizations across the world and was nominated to the Global Agenda Council of the World Economic Forum for 2012-2014 in recognition of his work.

He was conferred; 'Global Healthcare Leader of the Year' award in 2012 by the sheriff of Los Angeles; named the 'Thought Leader of the Year' three years in a row by ICT Post; Featured amongst the ’25 living Legends of Healthcare in India’ and is listed amongst the “100 Most Impactful Healthcare Leaders”.
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Data Analytics for cell and gene therapy by Dr. Ruchi Dass, @drruchibhatt



Cell and gene therapies are becoming more and more popular because of encouraging clinical results worldwide. Major pharma manufacturing companies have invested in the concept's commercialization worldwide. Recently, we read about Takeda’s license for commercialization of Aloficel (developed by TiGenix), Celgene’s acquisition of Juno Therapeutics or Gilead’s acquisition of Kite Pharma.

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Helping healing via the HORIZONS, The Tata Trusts Magazine

An ongoing transformation of Nagpur’s urban primary health centres has made affordable medical care more accessible to people

The patients sitting in the freshly painted waiting room have Amitabh Bachchan for company, even if it’s only a video of the movie star — with a message on measles vaccination — playing out on a television screen behind the reception desk. Red signboards provide an attractive pop of colour amidst the off-white walls. The well-appointed space could be part of a private hospital … except that it is not. It is the new look of a government-run urban primary health centre (UPHC) in Nagpur that has undergone a radical upgrade.

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India and Switzerland talk digital health by Aparna Kumaraswamy

A look back at the innovators and experts who showcased their technological solutions for enhanced healthcare delivery at the Digital Health Conclave 2019, curated by swissnex India

On 8th April, a group of healthcare enthusiasts got together to discuss the future of healthcare delivery, and the technologies that will assist in improving its quality. This motley group consisted of innovators, researchers, heads of large corporations, mid to senior level managers, and students from Switzerland and India. The one thing they had in common was their strong belief in the potential of technology to elevate the quality of healthcare and their openness to look across borders to find incredible innovations to this end. 

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Federated Personal Health Records – The Quest For #UseCases by Anukriti Chaudhary, @anukritichaudh2

In this blog post, we talk about one component of the National Health Stack – Federated Personal Health Records: its design, the role of policy and potential use cases


Overview

A federated personal health record refers to an individual’s ability to access and share her longitudinal health history without centralized storage of data. This means that if she has visited different healthcare providers in the past (which is often the case in a real life scenario), she should be able to fetch her records from all these sources, view them and present them when and where needed. Today, this objective is achieved by a paper-based ‘patient file’ which is used when seeking healthcare. However, with increasing adoption of digital infrastructure in the healthcare ecosystem, it should now be possible to do the same electronically. This has many benefits – patients need not remember to carry their files, hospitals can better manage patient data using IT systems, patients can seek remote consultations with complete information, insurance claims can be settled faster, and so on. This post is an attempt to look at the factors that would help make this a reality. 

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The obsession with tech in healthcare by Dr. Ravi Chandran Nayar

The logic that e-health can address the lack of medical professionals in India appears to be persuasive. But it is actually flawed

Medicine is an applied science and benefits from advances in basic and other applied sciences. In today’s world, technology itself is an applied science benefitting from basic scientific advances, defining our world and determining our world view of the present and future.

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Telemedicine and neurosciences by Dr Ganapathy Krishnan, @ApolloTeleMed

It is well documented that there is an acute shortage of neurologists and neurosurgeons in India and globally. Despite all efforts, it will be impossible to make available neurospecialists in all suburban and rural areas.

Simultaneously, there has been an exponential increase in the growth and development of Information and Communication Technology (ICT). Plummeting costs and unbelievable sophistication in the availability of user-friendly mobile video conferencing devices is making distance meaningless. Geography has become History! Worldwide, the ultraconservative health care industry, in particular, the medical community, has been uniformly slow to adopt and embrace the use of ICT to extend their clinical reach. In the last decade, however, specialists in all branches of neurosciences are slowly accepting the inevitable that telemedicine must and will have to be incorporated into the core of the healthcare delivery system. This literature review summarizes the current use of telemedicine in different subspecialties of neurosciences. The author defines the growth and development of clinical telemedicine in India with special reference to Neurosciences and attempts to show the stellar role telemedicine has to play in enhancing the services provided by doctors. As clinicians regularly using technology, it should not be difficult for us to convince our patients that today a virtual remote consult and management can indeed effectively substitute for a physical face-to-face encounter.

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A roadmap for Academia and Industry Collaboration in Digital Health by Bipinkumar G Rathod, @bipin4uk




Digital health Technology adoption in India is gaining momentum. Many digital health entrepreneurs are providing solutions for hospitals and patients. With India becoming more connected, by becoming one of the largest markets in terms of the mobile phone and broadband densities in the world, many Innovative solutions and Technology ecosystems are being developed for Digital Health interventions.

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India’s Health Leapfrog – Towards A Holistic Healthcare Ecosystem Part 1 by Anukriti Chaudhari @AnukritiChaudh2 - @Product_Nation

The leapfrog we envision is that of public, precision healthcare. This means that not only would every citizen have access to affordable healthcare, but the care delivered would be holistic (as opposed to symptomatic) and preventive (and not just curative) in nature.


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Top 8 Healthcare Predictions for 2019 by Reenita Das, @ReenitaDas - @FS_healthcare

What can you look forward to in healthcare in 2019? The debate expects to get hotter between AI vs. Physicians, Consumer vs. Clinical, Human empathy vs. Machine Intelligence as many new players enter the ecosystem


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Hospital Information System The two sides of a coin by Dr. Paridhi Mathur



Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. 

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A Summary of the proceedings at 2019 HITLAB: The Health Innovators Summit, New Delhi by Manish Sharma @msharmas

(source: HITLAB)
Impact of Frugal Innovations, AI, Remote Health Services, Vertically Integrated Technology Platforms and Care Delivery Platforms supported by technology, Blockchain based HealthTech solutions, Personal Digital Avatars, Diffusion of Digital HealthTech

The Health Innovators Summit, New Delhi by HITLAB in partnership with Department of Management Studies is a once a year conference held in February in India. 

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How to #Innovate for Impact? by Devmalya Sarkar @DevmalyaS

With digital technology advancing at warp speed or at the speed of startups these days, there's probably little that health-tech and digital health innovations can't potentially transform.


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#EHR in India: Challenges and Opportunities vis-a’-vis’ Ayushman Bharat by Dr. Oommen John, @oommen_john

As India is embarking on a journey towards providing Universal Health Coverage through multi-pronged approaches of reducing catastrophic out of pocket expenditure and increasing access to essential health services , it is envisaged that Health Information Technologies (HIT) / Digital Health would create enabling environments for addressing some of the system level challenges in healthcare delivery.


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Algorithms in #EMR by Dr. Joyoti Goswami @Joyoti10




Practicing physicians these days are barraged with a lot of technical jargon promoted by the Information technology professionals such as Big Data, Hadoop, Artificial Intelligence and Predictive analytics. For a physician not introduced to the these terms, the conversation is of little value unless there is a specified value in the clinical setting.


Pulitzer Prize winning author Siddhartha Mukherjee in his book ‘Emperor of Maladies’ has quoted “The greatest clinicians who I know seem to have a sixth sense for biases. They understand, almost instinctively, when prior bits of scattered knowledge apply to their patients—but, more important, when they don’t apply to their patients.”

So the accumulation of all the 6th sense of multiple physicians in the form of scattered notes in documents is the food for Big Data professionals, who curate this data manually with standard vocabularies and then analyse patterns that can help physicians make informed decisions at the point of care.

While Predictive Analytics is a huge winner if there is genomic data available, the fact remains that in current clinical scenarios, complete genomic sequencing is not yet the norm. They are still restricted to the areas of research as the cost of doing a complete sequencing of the genome is still about $ 1000 and very few institutions are prescribing it as a norm in routine clinical practice. Once that is in place, it can lead to a whole lot of practical applications and use cases making the use of technology like Big Data and Hadoop. 

Till then, use cases which use traditional computing powers like SQL and simple queries and algorithms running on top of it could be used to get the benefits of clean data and technology in the clinical workplace.

In an attempt to utilize Predictive Analytics and Artificial Intelligence in the world of EMR and healthcare data, a list of 5 practical clinical use cases and 5 administrative/Claims Related use cases of how data could be used at the point of care and integrated with the EMR is listed out below:

Clinical Usecases

1 Acute Kidney Injury

According to Statistics, 1.2 million people per year get AKI during a hospital stay and 300,000 people in the US die annually due to AKI, this is more than breast cancer, prostate cancer, heart failure and diabetes combined. 

The successful documentation and implementation of the AKI algorithm published by the NHS is a systematic and step by step program that can help reduce the incidence of Acute Kidney Injury and the mortality and comorbidities associated with it. The e-alert for AKI installed in the LIMS (Laboratory Information Management Systems) alerts physicians when a patients’ Serum Creatinine rises greater than 26 mmol/litre from a baseline within 48 hours or there is a rise of 50% or more in 7days and/or the urine output is < 0.5 ml/kg body weight/hour. https://www.england.nhs.uk/wp-content/uploads/2014/06/psa-aki-alg.pdf

2 Sepsis Management

Sepsis or SIRS (Systemic Inflammatory Response Syndrome) accounts for 20 to 30% hospital deaths and $15.4 billion in annual healthcare costs. Needless to say early diagnosis and treatment is critical to optimal care. 

In a study, it was found that 24% of infected patients with 2 or 3 qSOFA (or Quick Sepsis Related Organ Dysfunction Assessment) points accounted for 70% of the deaths. An automated EMR based Sepsis identification system helps to detect cases with sepsis. A sepsis sniffer algorithm in ED identifies patients who exhibit at least 2 of the 3 criteria of Altered Mental Status, Fast Respiratory Rate and Low Blood Pressure and initiates the Sepsis Order set if the results of the score warrant it. The content on the Sepsis Order Set helps to make sure that none of the key variables for the management of sepsis is missed out.
https://www.mdanderson.org/documents/for-physicians/algorithms/clinical-management/clin-management-sepsis-management-adult-web-algorithm.pdf

3 Initiation of Statins

Mayo clinic has implemented a single click decision support tool within the EMR to automate the calculation of 10-year atherosclerotic   cardiovascular risk and populate the statin choice decision.  Based on the risk score calculation parameters of the ASCVD (atherosclerotic cardiovascular disease), the tool populates whether the patient needs a statin or not and if yes, which statin would be the best choice. This is defined on the basis of an algorithm running which has historical data of similar patients along with their outcomes.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157431/

4 Algorithm to identify major cardiac adverse events while on statins

Cardiovascular disease is a leading cause of death worldwide and statins are largely prescribed for the same. A study was conducted using historical data to identify patients with MACE (Major Adverse Cardiac Events) while on statins for primary prevention.  This algorithm achieved a 90 to 97% positive predictive value for the identification of adverse cardiac events while on statins
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333709/

5 Detection of Diabetic Retinopathy

Diabetic retinopathy is the fastest growing cause of blindness and screening patients with diabetes for retinopathy has been mandated as part of all the Quality Measures such as MIPS, HEDIS and others. Google analysed a dataset of  128,000 images of the retinal fundus and used that to predict retinopathy based on the lesions present on the scan such as microaneurysms, haemorrhages, exudates etc. A blindfold comparison of the results predicted by the AI algorithm was compared with the results given by experienced ophthalmologists. The AI algorithm had an accuracy of 90% which is a huge win especially in areas where there is a shortfall of specialists to interpret the results.

In the areas of diagnostics especially interpretation of Imaging results, a lot of good work is going on and soon AI capabilities will lead decision making in the interpretation of reports.
https://ai.googleblog.com/2016/11/deep-learning-for-detection-of-diabetic.html

Physicians working closely with Data scientists can help create some exciting algorithms given that healthcare centres today have large volumes of data sitting in their EMRs. A physician with even 50 patient records of 2 years has sufficient information to derive useful insights on predicting interesting patterns on the future health and preventive measures therefore can be planned accordingly. The approach for healthcare institutions should be to first identify their most pressing problems and then evaluate if any kind of Prediction of solutions to those problems could lead to better outcomes in patient care. A well thought and organized approach could go a long way to develop algorithms that work well for the hospitals and ambulatory clinics to achieve their goals and have favourable outcomes for both the patient and the hospital.

Administrative Use Cases:

In Patient Registration systems and billing systems, the algorithms can have a different flavour, some of which are listed below:

1. Healthcare Utilization Management: Integration of hospital resource utilizations in different departments, risk management and quality assurance into a management dashboard in order to ensure the judicious use of the facility's resources. A review of the procedures and services rendered by the hospital and the resources used by them (such as rooms, timings, instruments, admin and clinical staff) can help to identify under utilized and spare capacity of the hospital. This can be inbuilt in the system with the help of algorithms so as to get an optimal effect.

2. Capturing Missed and Incorrect Charges: Many facilities lose revenue due to billing errors done manually. Billing amounts associated with each DRG (Diagnosis Related Group) can be used to create models and compare them with the actual billing. Outliers in patient invoices can help to identify the likelihood of missing or incorrect charges. The higher prioritized invoices can then be reviewed to confirm the charges, which may be incorrect, over billed or under billed.

3. Predicting denials: There is a constant tug of war between the providers and payers about the claims to be settled. Most of the denials are due to incorrect documentation or missing information, duplicate claims, service already paid as a part of bundled services or others, services not covered by payer and late submission of claims. All of the above can easily be avoided by streamlining coding and billing processes. The industry benchmark for medical billing denials is 2% and in practices between 5% to 10%. Reworking on denials has a cost associated, so having algorithms running within the systems can help to streamline these efforts.

4. Predicting wait times and No shows: Idle time in clinics, both for patients and providers can be frustrating. In departments where procedures are done, it is possible to predict treatment durations by looking at historical data. Similarly the number of no shows of scheduled appointments can be predicted by including variables like age of patient, severity of disease, previous appointment regularity of the patient and others. This could help the facility to take in additional patients on the free slots proactively.

5. Measuring Bad debt, Days in A/R and DNFB (Days in Total Discharged Not Final Billed): These metrics help to determine the effectiveness of the claims generation process and effectiveness of collection efforts.

A number of KPIs tracked by hospitals and payer systems can be in built within the Revenue Management Systems and Scheduling systems. These include patient costs, ROI of the facility, Average Patient Wait times, Average patient appointment scheduling times and Patient Satisfaction.

With the healthcare market growing and many EMRs, both locally created and branded companies in the play, it is eventually the content within the EMR that can make it user friendly. Many content companies offer excellent standardised algorithms and content that can be integrated within the EMR, but there always remains physician preferences based on specialty, region of practice and other personal preferences. The end user i.e. the physician ultimately needs to decide on what suits his practice and then have the algorithms accordingly tailored within the EMR.

Author
Dr. Joyoti Goswami
Healthcare expertise of over 20 + years. Clinician having worked in specialty hospitals as medical officer and currently in the Healthcare Information Technology domain since the last 10 + years. Have a good combination of clinical and technology skills in multiple areas. Worked with multiple EMRs such as Allscripts, GE Centricity, Athena health and Nextgen
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#2018InReview, Here's a Blog Post to say #ThankYou to our Amazing Authors at the HCITExperts Blog


Thank You !!

As 2018 came to a close, here is a review of some of the trend setting articles and insights shared by our amazing Authors at the HCITExperts Blog. 

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#RPA in Healthcare: The Path Ahead for Health IT Leaders By Sreejith Madhavan



Historically, healthcare industry has shown a reluctance to invest in technologies that did not come under the purview of diagnostics and treatment, or demanded by insurance payors (such as electronic claims submission). Anything that required cognitive (human) intervention or intuition was kept aside from the technological takeover. The unprecedented growth of life expectancy, the discovery of new drugs and treatments, and the ability of modern medicine to combat chronic ailments and epidemics have spurred the need for technological inclusion in multiple areas of healthcare.

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What does it take to build real-world #AI enabled healthcare solution? By Vijayananda J, @vijayanandaj



Development of new technologies has undoubtedly enabled several breakthroughs in the healthcare industry. To put it simply, it has revolutionised the growth of healthcare from nascent patient-care to accomplishing treatment of life-threatening diseases. High-performance computing and the availability of digital data have extended these remarkable outcomes explaining why AI-based healthcare solutions are at top of the funding lists and are continuously gaining traction.

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NITI Aayog’s National Health Stack - a Healthy Stack?! by Divya Raj @divyaraj1




Extraordinary problems need extraordinary solutions. And creating a country level IT infrastructure addressing challenges in India's Healthcare management for its 1.3 billion population definitely falls very well into that category. 

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