Digital Healthcare – Laws & Regulations in India

Digital health is using technologies to help improve individuals' health and wellness. These technologies include both hardware and software solutions and services, including telemedicine, web-based analysis, email, mobile phones and applications, text messages, wearable devices and clinic or remote monitoring sensors. Really it's about applying digital transformation, through disruptive technologies and cultural change, to the healthcare sector. Digital health is a multi-disciplinary domain involving many stakeholders, including clinicians, researchers and scientists with a wide range of expertise in healthcare, engineering, social sciences, public health, health economics and data management.

Digital Healthcare has been around in India since long but COVID-19 pandemic has put it in the spotlight and we are noticing mass adoption as 5 crore Indians accessed healthcare online in the last three months (Practo’s Insights Report, 18 Jun3 2020). In a significant move, the Ministry of Health and Family Welfare (“MoHFW”) on March 25, 2020, has issued the Telemedicine Practice Guidelines to provide healthcare using telemedicine and that is another major reason behind surge in online consultations. Also these Guidelines are one of the best guidelines ever published and the reason that telemedicine practice will stay in India. The Guidelines have made the practice of text/audio/video based medical care legal and regulated and thus have given platforms (mobile apps, web portals & social media) as well as doctors the standards to follow.

The legal and regulatory framework in India is/will be govern by following relevant acts / bills –

• Telemedicine Practice Guidelines by MCI & NITI Aayog, 2020
• Personal Data Protection Bill, 2019
• Information Technology Act, 2000 & Information Technology Rules 2011
• Clinical Establishment Act, 2010
• MCI Act, 1956 & MCI Regulations 2002
• Indian Medical Council Act, 1956 and Indian Medical Council Regulations 2002
• Drugs & Cosmetics Act, 1940 and Rules 1945
• Other Service Providers Regulations under the New Telecom Policy 1999

In September 2013, MoHFW notified the EHR Standards (Electronic Health Record Standards) for India.  Those standards were chosen from the best available & previously used standards applicable to International EHRs, keeping in view their suitability to and applicability in India.  Accordingly the EHR Standards 2016 document is notified and is placed herewith for adoption in IT systems by healthcare institutions and providers across the country.  The MoHFW facilitated its adoption by making available standards such as the Systematized Nomenclature of Medicine Clinical Terminology (SNOMED CT) free-for-use in India, as well as appointing the interim National Release Centre to handle the clinical terminology standard that is gaining widespread acceptance among healthcare IT stakeholder communities worldwide.

In addition, the MoHFW has proposed a new bill named DISHA (Digital Information Security in Healthcare Act) to govern data security in the healthcare sector.  The purpose of this Act will be to provide for electronic health data privacy, confidentiality, security and standardization.  The MoHFW, through the proposed DISHA, plans to set up a statutory body in the form of a national digital health authority for promoting and adopting: e-health standards; enforcing privacy and security measures for electronic health data; and regulating the storage and exchange of electronic health records.

One of the most immediate changes that health tech companies may need to be prepared for is the cost of compliance – with the Personal Data Protection (PDP) Bill 2019. As of the current interpretation of the text of the PDP Bill, 2019 (which effectively can get signed into law at any time) there is no period provided to affected companies to comply with the data protection measures in the Bill. The requirement of having a privacy-by-design system in place means that for a lot of companies the cost of compliance will go up as they would have to upgrade/overhaul their data protection systems and software. This change would be akin to the one experienced by European companies when they needed to comply with the General Data Protection Regulation (GDPR), but at least, in that case, there was a period prescribed within which companies were permitted to overhaul their security systems.

If any IT company or startup into Digital Healthcare plans to offer and add telemedicine/telehealth software to already existing software like healthcare CRMs, clinical software and patient management systems, have to incorporate all the relevant Acts & guidelines. It will not only help their clients but also will help companies because as per Telemedicine Practice Guidelines, technology platforms are obligated to ensure many instructions otherwise can be blacklisted.

Emerging Healthcare Trends in 2018

Digital transformation is set to overhaul the global healthcare industry. As we move into 2018, here are some emerging healthcare trends that will talk about how personalized medicine and value based care will be adopted in the healthcare ecosystem.

Precision Medicine
In cancer, there are misspellings or mutations in important genes that drive the cell to grow out of control and eventually move around the body. Different patients have different misspellings and hence do not benefit from same treatment. Because of the differences across patients the conventional one-size-fits-all treatment paradigms has low response rate. Further, precious time is lost while physicians progress through successive standard therapies with no guidance on which will prove efficacious. This is where Precision medicine will offer the promise of averting unnecessary treatment, minimizing drug adverse events, and maximizing overall safety to ultimately maximize the efficacy and efficiency of the healthcare system. The rapid identification of the most beneficial personalized therapy would transform the patient experience.

Real Time Monitoring
Non-invasive monitoring approaches will enable in collecting patient data longitudinally across multiple time points. This is enabled by various sensors to track patient vital signs 24x7 through wearable devices, complemented by blood and saliva monitoring techniques. This data availability opens up opportunities to improving healthcare - predict onset, identify right treatments and track treatment impact.

Real Time Personalization
A Cancer characteristic in a patient is not static. It changes with time due to treatment pressures and other reasons. Hence the treatment strategy for a patient will need to evolve with time. With the ability to monitor impact of treatment on a disease -analogous to software world this will create the opportunity to debug why a treatment is not working and to learn and course-correct. This real time personalization will create an updated paradigm based on real time personalization. 

Use of Big Data
With the rise of the Internet of (Medical) Things (IoMT), mobile and wearable devices being increasingly connected, working together to create a cohesive medical report accessible anywhere by your health care provider will surface. This data can be used to identify the risk factors and provide preventative treatment to the patients. It can be pooled and studied collectively to predict health care trends for entire cultures and countries. Together, volume, variety, validity, velocity, volatility, and variability of data will produce the ultimate challenges of Big Data to apply in practices such as precision medicine, among others. However, the visualization of clear and concise clinical action that provides value to the patient, physician, and healthcare system will emerge as an effective solution.
 
Artificial Intelligence
Big data aggregated provides opportunity to learn from past and predict the future. Some clinical questions are better suited to use of artificial intelligence techniques because of available datasets. Early disease diagnosis and automated interpretation of images and other reports are few applications where AI will add value. Further, AI will help healthcare practitioners in mining of the data to identify risk factors for providing efficacious clinical treatment.

Mobility and cloud
Mobility and cloud access is and will help patients and doctors interact better and real-time. Globally, majority of doctors already use smartphones and medical app and access drug info on smart phones on a regular basis. Hospitals, insurance companies, and doctor's offices are now storing patient medical records in the cloud, with patients able to access test results online 24/7.Now, mobile devices perform ECGs, DIY blood tests, or serve as a thermometer, for 'anytime, anywhere' users. Going forward, with increasing automation, patients can enter their health results/ check-up into mobile patient portals as well as provide[the said] information to doctors - right and fast.

Overall, with rise of digital technology adoption by the healthcare ecosystem, the overall clinical care delivery for patient empowerment will be more streamlined and thereby improve the way healthcare facilities function as well.

Big Data in Healthcare - Hype or Reality

The Big Data Questions

Big data is generating a lot of hype in every industry including healthcare. People are looking for answers to questions like:

    When will I need big data?
    What should I do to prepare for big data?
    What’s the best way to use big data?
    What is Health Catalyst doing with big data?

It’s important to separate the reality from the hype and clearly describe the place of big data in healthcare today, along with the role it will play in the future.

Big Data in Healthcare Today



A number of use cases in healthcare are well suited for a big data solution.
Some academic- or research-focused healthcare institutions are either experimenting with big data or using it in advanced research projects.
This presentation will examine what’s being done to simplify big data and make it more accessible.

A Brief History of Big Data in Healthcare

In 2001, Doug Laney, now at Gartner, coined the term “the 3 V’s” to define big data:

• Volume
• Velocity
• Variety

Other analysts argued that this is too simplistic but for this purpose let’s start here.









EMRs alone collect huge amounts of data, but according to Brent James of Intermountain Healthcare most of the data is for recreational purposes.
Our work with health systems shows that only a small fraction of the tables in an EMR database (perhaps 400 to 600 tables out of 1000s) are relevant to the current practice of medicine and its corresponding analytics use cases.

There is certainly variety in the data, but most systems collect very similar data objects with an occasional tweak to the model.
That said, new use cases that support genomics will certainly require a big data approach.

Health Systems Without Big Data

Most health systems can do plenty today without big data, including meeting most of their analytics and reporting needs.
We haven’t come close to stretching the limits of what healthcare analytics can accomplish with traditional relational databases—and using these databases effectively is a more valuable focus than worrying about big data.



Most healthcare institutions are swamped with some very pedestrian problems such as regulatory reporting and operational dashboards.
As basic needs are met and some of the initial advanced applications are in place, new use cases will arrive (e.g. wearable medical devices and sensors) driving the need for big-data-style solutions.

Barriers Exist for Using Big Data

Expertise and Security

Several challenges with big data have yet to be addressed in the current big data distributions.
Two roadblocks to the general use of big data in healthcare are the technical expertise required to use it and a lack of robust, integrated security surrounding it.


Expertise  

The value for big data in healthcare today is largely limited to research because using big data requires a very specialized skill set.
Hospital IT experts familiar with SQL programming languages and traditional relational databases aren’t prepared for the steep learning curve and other complexities surrounding big data.

Data scientists are usually Ph.D.-level thinkers with significant expertise.
These experts are hard to come by and expensive, and only research institutions usually have access to them.
Data scientists are in huge demand across industries like banking and internet powers with deep pockets.

The good news is, thanks to changes with the tooling, people with less-specialized skillsets will be able to easily work with big data in the future.
Big data is coming to embrace SQL as the lingua franca for querying. And when this happens, it will become useful in a health system setting.