Month: July 2018

New Data Protection Law Proposed in India! Flavors of GDPR by TMT Practice Team at Nishith Desai Associates



NEW DATA PROTECTION LAW PROPOSED IN INDIA! FLAVORS OF GDPR 
The much-awaited Personal Data Protection Bill, 2018 (“Draft Bill”) was released by the Committee of Experts entrusted with creating a Data Protection Framework for India (“Committee”) on Friday evening.


The Committee, chaired by retired Supreme Court judge, Justice Srikrishna, was constituted in August 2017 by the Ministry of Electronics & Information Technology, Government of India (“MeitY”) to come up with a draft of a data protection law. After over a year of deliberations and a series of a public consultations followed by release of a white paper with preliminary views, the Committee has released a Draft Bill. The Draft Bill is accompanied by its report titled “A Free and Fair Digital Economy Protecting Privacy, Empowering Indians” (“Report”) which provides context to the deliberations of the Committee.

MeitY as the nodal ministry may accept, reject or alter such Draft Bill. Thereafter, the Draft Bill would need to be approved by the Union Cabinet before it is introduced in the Parliament for deliberations.

Some of the key highlights of the Draft Bill are:

  • Extra-territorial application i.e. the Draft Bill is to apply to foreign data processors in so far as they have a business connection to India or carry on activities involving profiling of individuals in India.
  • Differential obligations imposed based on criticality of data, i.e. differing obligations for Personal Data and Sensitive Personal Data;
  • Obligations of the Data Processor : Notice (that is clear, concise and comprehensible), Purpose Limitation and Collection Limitation, maintaining data quality, storage limitation;
  • Grounds for processing in addition to consent include use for employment purposes as well as emergencies.
  • Intended to be made applicable to the State as well as private parties.
  • Child Rights: Child is defined as someone who is less than 18 years of age. Profiling, tracking or behavioral monitoring of or targeted advertising towards children is not permitted.
  • Rights of the Data Subject: Include Data Portability, Right to be forgotten as well as the right to correction of the data etc.
  • Concept of Privacy by design and a data breach notification have also been introduced;
  • High Risk Data Processors – A mandatory registration requirement has been imposed on data processors who conduct high risk processing. Such processors are required to implement: Trust Scores, Data Audits as well as a Data Protection Impact Assessment
  • Data Localisation: A copy of all Personal Data must be stored in India; additionally the Government may notify certain types of personal data that should be mandatorily be processed only in India. The Government has retained with itself the power to exempt storage of copies of Sensitive Personal Data, in some cases.
  • Cross Border Data Flows: In addition to consent cross border transfers would also require the use of (a) model clauses; and (b) possible adequacy requirements, i.e. transfer to jurisdictions approved by the Government;
  • The Data Protection Authority of India (“Authority”) appointed under the Act will provide or endorse Codes of Practices.
  • GDPR Style Penalties: Upto 4% of global turnover in some cases;
  • Criminal penalties also introduced for limited cases;
  • Phased manner of implementation once the law is implemented.


To summarize, whilst we believe that the Draft Bill does have its share of positives, in several places the Draft Bill is either ambiguous / not clear or imposes excessive obligations on Data Fiduciaries and prescribes disproportionate punishments. Several factors are left to be determined through Codes of Practices or to be determined by the Government at a later stage. Therefore, at this stage the full impact of the proposed law cannot be comprehended in entirety.

In several respects, we note the Draft Bill appears to have borrowed heavily from the recently notified E.U. General Data Protection Regulation (“GDPR”). Given the infancy at which the GDPR is at this stage, it would be imperative that law makers provide for enough flexibility for the law to be altered on the basis of global experiences. Further, we find that even the current basic law under the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011 (“2011 Rules”) has yet not been implemented fully even after 7 years. Therefore, implementation will be key to this fairly detailed and somewhat cumbersome law.

We hope that the law is made more balanced by diluting some of the draconian provisions as well as by issuing clarifications on the points that are not clear, after public consultation. Therefore, ideally, once the MeitY finalizes the draft, it should place such law in the public domain and provide stakeholders an opportunity to provide further inputs, before the law is placed before parliament.

We have set out in our detailed analysis below the possible implications that it may have on businesses, including offshore companies doing business in India. As we continue to read, debate and delve deeper into the wording of the law, our views on several of these issues may evolve.

To summarize, while the Draft Bill does have its share of positives, in several places the Draft Bill is either ambiguous / not clear or imposes excessive obligations on Data Fiduciaries and prescribes disproportionate punishments. It also seems to have certain unintended consequences for start ups/non digital businesses in terms of imposing exposing them to excessive compliances. 

Our detailed analysis of the Draft Bill is available here.

Please do join us this Tuesday (31Jul 2018) and / or Wednesday (01 Aug 2018) at our Webinar where we discuss the impact that the Draft Bill may have. The registration link for the same is available here.

Email the Technology & Privacy Law Team and You can direct your queries or comments to the authors

The article was first published here,  its been republished on the HCITExperts Blog with the authors permission. 

Additional Reading:
1. Regulatory Essentials for eHealth in India by Dr. Milind Antani, Nishith Desai Associates: 
https://blog.hcitexpert.com/2018/03/regulatory-essentials-for-e-health-in-india-Dr-milind-antani.html

Author
TMT Practice Team at Nishith Desai Associates

Nishith Desai Associates is a research-based Indian law firm with offices in Mumbai, Silicon Valley, Bangalore, Singapore, Mumbai BKC, Delhi, Munich and New York that aims at providing strategic, legal and tax services across various sectors; some of which are IP, pharma and life-sciences, corporate, technology and media

#Blockchain for HealthCare Equity by Arnab Paul, @iArnabPaul

In a digital Age when cars drive themselves and CEOs hold meetings across continents in virtual reality conference rooms, engagement of the disenfranchised is a less attractive endeavor than the sleek apps, making it an outlier in the realm of tech solutions.

In our endeavour to promote digital india it should be our collective effort to bring healthcare to the disenfranchised and to the people who slip out of the cracks.

Most of us just don’t bother to take care of the elephant in the room, its time all the stakeholders joined hands and come up with a solution. For me personally Equity is of paramount importance in healthcare.The lack of focus on vulnerable populations in patient safety discounts the significance of the many lives lost, all precious to those who love them. we have yet to place strategic emphasis on the need to protect all. A man’s life lost to medical error then disguised as a heart attack, either intentionally or because of unconscious prejudice about the depth of his pocket, is more than a patient safety event. 
For the millions of people who have been exposed to discrimination based on their spending capacity and limited access to resources and denial of equality in humanity, such an event adds insult to tragic injury.We must connect in ridding our health system of all forms of inequality and ensuring that all people are protected from harm equally.
As hospitals and care systems work to improve quality of care and prepare for coming changes in the health care field, the ability to fully understand their patient populations and communities is critical. Collecting and using ethnicity, language, spending capacity data will help hospitals and care systems understand their patient populations and address health care disparities. While many hospitals are successfully collecting REAL data, fewer are effectively stratifying the data to shed light on health care disparities,
We need to systematically collect REAL preference data on all patients. We need to use REAL data to look for variations in clinical outcomes, resource utilization, length of stay and frequency of readmissions within our hospital. We need to compare patient satisfaction ratings among diverse groups and act on the information. Above all we need to actively use REAL data for strategic and outreach planning for the underprivileged.
Patient satisfaction is not a clearly defined concept, although it is identified as an important quality outcome indicator to measure success of the services delivery system
There is no clear consensus between the literatures on how to define the concept of patient satisfaction in healthcare.
In Donabedian’s quality measurement model
patient satisfaction is defined as patient-reported outcome measure while the structures and processes of care can be measured by patient-reported experiences
For everything in life we need some kind of metrics, some tools to measure the clinical outcome and the patient satisfaction. So to make up for it may I suggest we incorporate Tech enabled, Blockchain optimized patient feedback mechanism.
So what is the solution, how do we propose to go about it, well unlike Press Ganey & HCAHPS (the Hospital Consumer Assessment of Healthcare Providers and Systems), Press Ganey has stated that a minimum of 30 survey responses is necessary to draw meaningful conclusions from the data it receives and that it will not stand behind statistical analysis when less than 30 responses are received. 
If we all incorporate a blockchain Ecosystem & go truly real time in the patient feedback mechanism it would greatly enhance the whole patient experience and maybe help to manage solve some of the issues in real time. Wouldn’t it be just great if we incorporate Blockchain in the patient feedback loop, we wouldn’t have to wait for 30 odd surveys to be analyzed we could just go ahead and fix the situation right away if it warrants an action.
Another major issue is NO show and Missed Appointments
One study estimates, in US alone missed appointments cost US healthcare providers up to $150 billion a year.There have been instances that a Clinic loses money because of No Showand missed appointments.Patients not showing up can be costly to the health-care system. Offices lose out on revenue, and delaying care can lead to more expensive treatments later on.

“We very much believe it’s going to take a collaborative effort, and we think that this kind of technology integration is going to be a critical path for being successful in terms of breaking down those barriers for access to transportation for the patient community.”  David Baga, CBO, Lyft

Allscripts, Lyft and few other companies have joined hands to address this problem. The companies said they hope working together will reduce the number of people who miss medical appointments because of transportation issues.
But interesting it was found in another study giving poor people free use of ridesharing services like Uber and Lyft for doctor appointments doesn’t make them any less likely to become no-shows than patients who have to find their own way there, a U.S. study suggests.
So what are we missing here, I believe incentivising ( tokens ) is the key and Blockchain could play a major role. Blockchain in itself is not a panacea for all things healthcare but it certainly holds the key to transform the current healthcare service delivery mechanism and make it more transparent and efficient.
Ehealth or no ehealth, if its not able to solve the issues of equity & empathy than its no value prop only noise, maybe it would help become a excellent facilitator in healthcare delivery but it sadly would not be able to solve the core issue of equity and empathy.
The concluding part follows:
How Blockchain could be a gamechanger for healthcare

Author

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Arnab Paul, CEO, Patient Planet

Globally-minded systems thinker, action-oriented and inspired toward optimizing health outcomes through innovation, creativity, cooperation. Passionate about facilitating the alignment among technology, people and processes to ultimately improve patient experience and the functioning of healthcare.

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Zen Clinicals: An Activity & Workflow based solution (3 of 4)



Part 3 of 4

Sharing some of the UI screens that I had envisaged for the Zen Clinicals system. The core premise of the system is to be data driven and workflow driven and NOT BE a transactional system wherein a sequential set of activities or tasks are carried out.

Zen Clinicals ensures there are actionable insights that are presented to the right care providers at the right time. 

Welcome you to review the first two parts of this blog: 

Part 1: https://blog.hcitexpert.com/2015/12/zen-clinicals-1of3.html

Part 2: https://blog.hcitexpert.com/2018/07/zen-clinicals-patient-care-pathways-by-manish-sharma.html

Zen Clinicals: A Patient Care Pathways Solution (4 of 4)

A Clinical Care Pathways Workflow & Activity Orchestration

Overview

Clinical pathways are structured multidisciplinary care plans which address specific clinical scenarios and help to standardize and coordination of care.
The patient care pathways solutions have become an important clinical guidelines implementation tools in hospital settings. They have the ability to provide, not only a standardised care to the patients’ presenting same/ similar diagnosis, but also provide the ability to handle the variance from the defined care pathways for various specialties and diagnosis.
The care pathways also provides the ability to coordinate care of a patient across various user groups and user roles, like the group of nurses across shifts and across roles like doctors, nurses and administrative staff.
The Care Pathways aim to optimize the efficiency and quality of care
The care pathways solution that we propose to develop have the following features.

Clinical Data Repository

At the heart of our Patient Care Pathways solution is a clinical data repository that maintains a longitudinal patient care record. The Patient CDR is a data warehouse of the Patient’s clinical data that has been stored in the clinical data repository to enable data analysis across the patient episodes and visits.
The Clinical Data Repository provides the ability to organically analyse data from ‘similar’ patients across same or similar set of data points like diagnosis, test results and clinical studies.
The clinical data repository converts all the data captured in various connected systems to a analysable data point for the care pathways solution. The data captured in the external system
The clinical data repository is a the heart of the care pathways solution that contains information that has been stored in standardised format using the standard healthcare coding systems like ICD10, SNOMED CT, ICD10 PCS and others as applicable in the clinical scenario.
In-built into the Clinical Data Repository are latest big data analytics capabilities that are required to generate the statistical analysis of the adherence to the quality and efficincy of the Care Pathway.
The clinical data repository has the ability to generate, out of the box, all the analytical reports and predictive modelling required for todays’ clinicians to make the right decisions at the right time.
The Clinical Data Repository allows for the following features and functionalities that are relevant to the Care Pathways solution
  1. Allows for analysing historical outcomes from clinically similar patients, e.g. patients like me type of scenario matching
  2. Display the variance and outcomes for the patient specific pathway and across various patients

Care Pathways Designer

The Care Pathways designer is a visual tool that allows the user to define the workflow of activities based on a start condition. The Start Up condition for a Care Pathway could be a Code Red alarm for quarantine or it could be a set of questions that a care provider answers to based on the patient they are treating for, for instance, chest pain.
The Care pathways designer has at its core the following:
  1. Activities
  2. Activity Groups
  3. Users
  4. User Groups
  5. Workflow Designer
Care Pathways Designer Features:
  1. The care pathways designer is a tool that allows the care pathways design team to define the standardised care templates of activities that should be scheduled for a patient presenting a chief complaint or being treated for a chronic condition.
  2. The care pathways designer has a set of pre-defined activity categories. For example, Appointment Scheduling, Laboratory Orders, Radiology Orders, Pharmacy Orders and many more. Each of the activity categories are defined based on the corresponding activity that needs to be instantiated at the host system, such as an EHR or a HIMS Solution. The Patient Care Pathways solution allows the users to define these activity categories depending on the host system.
  3. The Care Pathways Designer tool allows the users to define the workflow for the care pathways. The tool allows the user to define rules and the activities (or activity groups) that will be instantiated depending on the condition of the rule.
  4. The Care Pathways designer tool also has the ability to define various outcomes and the activities that need to be performed to achieve those outcomes.

Rules Designer

The clinical pathways designer will have a rules designer that allows the users to select the various activities and activities groups to be the outcome and action targets for a clinical rule.
For instance, if the doctor selects a certain type of schedule H drugs to be ordered for a patient and the doctor does not have the authorization to order schedule H drugs, the order will be sent to the next level of authorization within the patient care team.
The rules can be included into the care pathways designer as workflow activities and decision criteria.

User & User Group based Task Lists

The system will have a user specific task list that will inform the user on the various activities that a user (nurse, doctor, admissions and billing users) needs to perform vis-a-vis a patient. The Task list will help the user to complete the tasks that have been generated based on the various active care pathways for the various patients.
The system also has the ability to share the generated tasks between a group of care providers. This allows for the nurses taking care of the patients across the various shifts to review the tasks that have been completed or not completed during an earlier shift.

Care Pathway Dashboard

The system will have a comprehensive Care Pathway Dashboard. The dashboard will display the following:
  1. The Care Pathway adherence Index displays the variance or conformance of a care pathway when applied to multiple patients.
  2. The Care Pathway patient adherence Index, this displays the effectiveness of a care plan when applied to a specific patient.
In both the above scenarios, the care plan adherence Index will display the deviations, the variances and adherence levels to the defined care plan. It will also list out the activities that were done in addition or in variance of a defined care plan.
In addition to the variance and conformance metrics the Care Pathway Dashboards will display to the users the following:
  1. The Patient Specific Care Pathway Activity Scheduler: Displays the day by day list of all the activities that have been defined (scheduled) in a care plan. It also has the ability to showcase the activities by a shift.
  2. Care Pathway timeline: Displays the care plan timeline and recorded variance and non-conformance detection
  3. Outcomes Recording: The Care Pathway solution also allows the users to record the outcomes for each of the planned activities and the Outcomes defined for the Care Pathway.

Care Pathway Push Notifications & Alerts Center

The Care Pathway Push Notifications and Alerts center is an important part of our solution. The Care Pathway push notification system identifies the most important and high priority tasks that need to be performed for each of the patients. The users are alerted about these high priority tasks on their associated mobile devices.
The Care Pathway Push Notifications have been designed to be non-obstrusive and presented in a way the user has configured these to be delivered to them. This is done to ensure there is no alert-fatigue generated based on our notifications and alerts center.
In addition to the Push Notification for tasks to be performed on a high priority, the Care Pathways solution also has the ability to provide alerts of outstanding or upcoming, completed or activities in variance of the defined Care Pathway to relevant team members.
Each alert and push notification will be configured with a level of priority (that can be assigned to the alert type or determined by the system) and depending on the Level of Priority of that alert and notification, the system will present the alert to the user.

Additional Resources & Standards Definitions:

  1. The care pathway models in the tool are based on the following industry standards:
    1. OMG Case Management Model and Notation (CMMN), and
    2. HL7 GELLO and
    3. vMR

Care Pathway Examples:

carepathwaychestpain.gif
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Artificial Intelligence #AI can help address current healthcare challenges in India, Dr Sandeep Reddy @docsunny50

Earlier this year, while making a keynote speech at an Artificial Intelligence (AI) in Health conference in Dubai, I mentioned that AI techniques can be used to address some of the intractable health issues in developing countries. 


This comment was picked up by a journalist of an online news site and reported as the headline of a news item covering the conference. However, the journalist also mentioned I hadn’t provided any details to qualify my comments. To my defence, the focus of my speech was not about this topic. Subsequently, in a conversation a week ago with the founder of an US-based AI health start-up, we discussed the multitude of opportunities in using AI enabled health services in developing countries and how few are aware of these opportunities. These circumstances and a reminder from the editor of this site about my promise to contribute an article has now led me to articulate the benefits of AI in health in the context of developing countries. Here, I use India as a typical developing country but many of the processes I discuss can potentially be used in other developing countries.


First, let us discuss some of the common issues that health services in developing countries face. A common grievance of health services is the lack of qualified workforce to treat and manage patients. Where health services have qualified personnel, they are overloaded with patients affecting the quality of the service they provide. The other common issue is the urban-rural maldistribution of qualified physicians. The preference of physicians to practice in urban health centres has led to a skewed distribution favouring urban centres and disadvantaging rural communities. In spite of government initiatives to push quality rural health services, the urban-rural divide is to stay. Another prominent issue is the variability in the quality of health services provided in different parts of the country and sometimes within the same region or city. This inconsistency is because of poor monitoring of health services by national accreditation bodies or poor compliance with quality standards by health services. Further, outbreaks of infectious diseases because of mainly environmental reasons has become alarmingly frequent in developing countries. Poor surveillance infrastructure means these outbreaks can progress to epidemics in a span of days. These aren’t the only health system concerns in developing countries but are areas that I think will benefit from the application of AI techniques.


India with its massive population of 1.35 billion (2018 population estimate) is in dire need of strong health infrastructure and government policy to service the nation’s health needs. India, just like many developing countries, has significant challenges in delivering this requirement. A combination of increasing burden from chronic diseases, a large ageing population, qualified personnel shortage, urban-rural divide, low government investment in health, inadequate health insurance coverage and variable quality of health service delivery have contributed to this state of affairs. However, the Government of India has been lately active in firming up the health policy and strengthening the health infrastructure. One of the major initiatives of the government was the release of the National Health Policy last year with an aim to reinvigorate the healthcare delivery in India by increasing health spending, establishing national quality standards, promoting evidence-based healthcare and introduction of digital health initiatives. With regards to the latter objective, the intention to set up a National Digital Health Authority and promote interoperable Electronic Health Record systems across India will create a strong foundation for digital health innovations to be applied. This digital platform will also provide opportunities for Foreign Direct Investment (FDI) and contribute to further growth of digital health in India. As digital health initiatives ramp up in India, opportunities for application of AI will also open up.

So how would AI applications help the Indian health system? Earlier, I discussed the healthcare delivery challenges developing countries face. The same difficulties apply to India too. AI systems driven by deep neural networks and computer vision have matched accuracy levels of human clinicians in interpreting radiological, fundoscopic and histopathological images. Intelligent agents are also being used to mine data and analyse electronic health records to assist clinicians in the medical diagnosis and predicting mortality of patients. Also, machine learning and natural language processing driven mobile applications are being used to communicate with patients and aid medication adherence, healthy lifestyles and schedule visits to doctors. Further, AI applications are being used in hospitals to predict the length of stay of patients and formulate treatment plans for them. All of these developments are detailed in several academic journals and the media. Application of these agents will have a profound effect on the Indian healthcare landscape, where a shortage of qualified specialists and diagnostic centres abound. While the AI systems may not be able to replicate all the capabilities of the medical specialists, it will in combination with telemedicine approaches be able to increase healthcare access for underserved communities and alleviate the burden of overstretched health services.


AI systems can also aid in the improvement of the quality of healthcare by reducing the variability of healthcare delivery and enabling evidence-based practice across the country. By incorporating government sanctioned and thoroughly evaluated AI applications in healthcare delivery, standardisation of healthcare delivery can be achieved. With inconsistency in healthcare delivery and non-evidence-based practices being common in India, roll out of authorised clinical decision support systems that run through machine learning processes will contribute to standardisation of healthcare delivery. Also, AI systems through ongoing analysis of ecological, biogeographical and public health data can alert authorities about outbreaks of infectious diseases and help contain the spread. For example, in recent years machine learning has been used to identify sources of outbreaks. During the Ebola outbreak in Africa, machine learning was used to analyse ecological data to determine the bat species harbouring Ebola virus and contain the spread of the disease. Thus, AI agents can also be used to strengthen India’s communicable disease surveillance infrastructure.

While the use of AI applications presents significant promise for the Indian healthcare system, one has also to be cognizant of the challenges in applying AI approaches. AI applications rely on a robust digital health foundation including ongoing access to electronic patient data and patient/population information management systems. With the Indian digital health infrastructure being nascent at best, widespread roll-out of AI applications can be a challenge. Also, with the low number of qualified health informaticians, machine learning trained data scientists and AI focused entities in India, there may be increased reliance on overseas companies to support the roll-out of AI applications. There are also issues like bias, lack of contextual reasoning and explainability problems that accompany AI applications. However, with advances in AI technology some these issues have now been addressed with number of solutions available.


To harness the benefit of AI approaches, the Indian government has to formulate a definitive AI strategy. A strategy that amongst many other things outlines the regulatory framework and implementation strategy for the roll-out of AI in India. The immense benefits that come through application of AI can be only be realised through the boldness and proactiveness of the Indian government. By pushing forward a national AI strategy and setting up an AI enabled healthcare delivery system, India can be a leading example for other countries as to how critical healthcare challenges can be addressed through AI approaches.

Author
Dr Sandeep Reddy, MBBS DPH MSc MMgmt MBAcert PhD CHIA

I am a Certified Medical Informatician, Health Program Evaluator and Artificial Intelligence in Medicine (AIM) Researcher with education, training and experience from leading institutions and various parts of the world. I am currently focused on the research and application of artificial intelligence techniques and program evaluation methodologies in the healthcare sector. In addition to peer reviewed and non-peer reviewed articles, I have authored two books and in the process of completing another one. More about me can be found here: http://www.drsandeepreddy.com

The Integrated Disease Surveillance Program (IDSP ) of India story by Dr. Pramod Jacob

Considering the Nipah virus containment story recently, I thought it would be appropriate to write about the IDSP program in India, as it had a major role in this containment.

The Integrated Disease Surveillance Project (IDSP) was launched in November 2004 with the assistance of the World Bank, to identify and respond to disease outbreaks and epidemics at an early stage, preferably before an event becomes an epidemic.


There were 4 Components:
a. Decentralisation and integration of surveillance activities through establishment of surveillance units at district, state and central levels
b. Human Resource Development with training of State Surveillance Officers, District Surveillance Officers, Rapid Response Teams and other relevant staff
c. Use of information technology for collection, compilation, analysis and dissemination of data and
d. Enhancement of Public Health Laboratories.

The following Objectives were to be met
1. Cover limited number of diseases of public health importance which needed public health response
2. Implement multiple methods of surveillance
3. Be a proactive program with timely response at all levels i.e. Be an Early Warning and Response (EWAR) program
4. Use Information Technology to facilitate information gathering, collation, analysis and dissemination
5. Decentralise and have states take ownership and
6. Centre be responsible for coordination, quality control, policy formulation, finance management and technical assistance.

It was realised that though the healthcare infrastructure in India had grown over the years, disease surveillance had not got the required attention in the past, resulting in late detection of disease outbreaks with related morbidity and mortality. One of the main reasons for this shortcoming was the time-consuming and labour-intensive manual methods of data collection, transmission, analysis and feedback for response with paper. 

Hence a countrywide Information and Communication Technology (ICT) network was established under IDSP with the help of National Informatics Centre (NIC) and Indian Space Research Organisation (ISRO). This IDSP network connects the District Surveillance Offices to the State Surveillance Offices which then connects to the Central Surveillance Office at the National Centre for Disease Control (NCDC). 

The network is also presently being deployed to CHC and even PHC levels in some states. The network is used for data entry, compilation, analysis and feedback from data coming in from the sub centre level and above. It has video conferencing ability to help in meetings and training sessions. Furthermore, there is an IDSP portal ( www.idsp.nic.in ), which is a one-stop portal for data entry, reports, outbreak reporting, data analysis and training modules related to disease surveillance.

The IDSP program has three methods of surveillance 
1.  Indicator Based Surveillance 
2.  Event Based Surveillance and 
3.  Media Surveillance. 

Briefly describing each of these: –

Indicator Based Surveillance

There are three levels where these indicators are collected: – 

S form – this form is filled by the sub-center health worker and collects collated details on conditions such as Fever, Cough, Loose watery stools, Jaundice and Acute Flaccid Paralysis (AFP). These forms are submitted to the supervising primary care centre once a week and fed into the IDSP system via the District Surveillance Unit (DSU).

P form – this form is filled by the primary care providers and collects collated data on about 20 different conditions including the above plus additional conditions such as Pertussis, Diphtheria, Leptospirosis etc. This is also submitted on a weekly basis and uploaded into the IDSP system weekly via the DSU.

L form – this is the form collected from public health (and private) Labs for positive test results for specific diseases such as Dengue, Japanese encephalitis , Cholera etc. The difference in these forms are that for each positive result, further details such as the patient name, age, address, test done and lab confirmation diagnosis is also recorded. These forms also go into the IDSP network via the DSU on a weekly basis.

For more details about information on each of these forms please visit the following link 
http://idsp.nic.in/index1.php?lang=1&level=1&sublinkid=5850&lid=3781

From the DSU the information gets instantaneously transmitted to the State Surveillance Unit (SSU) and then the Central Surveillance Unit (CSU) via the IDSP network. This network has been effectively working since 2010. The result is that the CSU based in the National Centre for Disease Control (NCDC), has been publishing nationwide outbreaks of these specific diseases on a week by week basis about a month later (On June 20th could see the outbreaks that occurred in the week of May 14th to May 20th) – here is the link 
http://www.idsp.nic.in/index4.php?lang=1&level=0&linkid=406&lid=3689

Event Based Surveillance

Since public health only covers about 30 percent of the population – it was realized that there had to be a mechanism in place to identify and respond to events such as suspected outbreaks like epidemics or events that could endanger the public.
An incident maybe reported through the rumour registry or through review of indicator-based surveillance data or through the media.  Whenever there is such an incident reported and verified, there is an Early Warning Signal (EWS) protocol carried out, to which a Rapid Response Team (RRT) of a district investigates and takes the appropriate action.
The Rapid Response Team in a district is a multi-faceted team looking into various aspects of a potential outbreak. The suggested members would be an epidemiologist, a clinician and a microbiologist/virologist. The RRT is not a permanent team but is formed when the need arises from existing resources in the concerned district under the aegis of the DSU. After an initial investigation by the concerned Medical Officer with filing of an Early Warning Signal/Outbreak report, the RRT verifies the outbreak through Medical and Lab investigations, with the epidemiologist studying the epidemiological and environmental aspects of the outbreak including source of the problem and routes of transmission.
Once the answers for the causal agent, source of infection, transmission pattern and people at risk are found, the RRT comes up with the specific recommendations and action plan to curtail the outbreak to be implemented by the concerned public health staff which can include state and central (NCDC) levels if needed. This may include steps such as identifying infection isolation points, enforcing infection control protocols, organizing logistics such as special protective gear, burial protocol and sites, tracking and quarantine of contacts, ensuring disease awareness and precautions to be taken by the public. It was this mechanism that played a major role in control and containment of the recent Nipah virus outbreak in the country.

Media Surveillance

NCDC and some states have a Media Scanning and Verification Cell that monitors Global, National and Regional electronic and print media for reports on suspected outbreaks or unusual health events. It disseminates such reports to the concerned districts digitally for verification and follow up. The major part of the screening is manual with a process in place for filtering genuine from fake news. There are plans afoot to bring in automation into the screening process.

In summary it is a combination of all the above three methods that bring about the Early Warning mechanism for outbreaks and potential epidemics in India. While there is much room for improvement- the IDSP program has proven the effectiveness of a nationwide IT network and in-fact can potentially be upgraded to be the Healthcare IT highway for the country.  


“Dedicated to the IDSP program and public health staff of India – who do so much with so little. Often criticized, seldom appreciated, a big heartfelt thank you” – Dr. Pramod Jacob

Author
Dr Pramod D. Jacob (MBBS, MS- Medical Informatics)

After completing his medical degree from CMC Vellore and doing his Master of Science in Medical Informatics from Oregon Health Sciences University (OHSU) in the US, Dr Pramod worked in the EMR division of Epic Systems, USA and was the Clinical Systems Project Manager in Multnomah
County, Portland, Oregon. He went to do Healthcare IT consultancy work for states and counties in the US and India.

At present he is a Director and Chief Medical Officer of dWise Healthcare IT solutions. He was also a consultant for WHO India in the IDSP project and for PHFI for a Non Communicable Diseases Decision Support Application.

4 hints to get started with #AI in your company by Devesh Rajadhyax @deveshrajadhyax

Most companies are working on Digital Transformation today, and Artificial Intelligence is a critical part of that transformation.

Two questions immediately present themselves-
1.    What is Digital Transformation and how it is different from the IT/ICT transformation that is happening since for than four decades?
2.    Why is AI a critical part of this transformation?

Let me take the first question.

Digital Transformation is actually a cognitive revolution. It is a more humanlike way of making sense of the world around. And this is our clue to the most important difference between IT and Digital-
IT systems are not humanlike. They don’t try to make sense of the world around them. They create a small world of their own and everyone follows the rules of that world. The input has to be given in the way they demand and output will be available in forms that they prefer. You better fall in line.
Digital, or Cognitive systems would want to fit into the world they find themselves. To understand this, see how we humans manage our operations. We take in all the signals from the real world. We see, hear, feel, smell and then speak, show, move and push at the world around us. That’s what Digital systems aim to do. They want to process all the available inputs and then interact in a natural way. That’s why they hope to solve problems much closer to our life.’
Now let’s turn to the second question, why AI is so critical in this transformation.
I think the above distinction must have given you an intuitive answer. Because we are aiming for a humanlike way of interacting with the world, we need AI to do the processing. We also need sensors (IoT) and motors (Robotics), displays (VR) and so on. You can see how well the various Digital Technologies fit in this paradigm.
But even before AI, there is a big big factor that drives this whole transformation, and that factor is Data.
Since we are talking about real world data, we are talking huge huge volumes in forms that we never processed before. Vision, audio, waveforms, text, handwriting and many more types of data need to be captured (again IoT), stored and processed (Cloud, Big Data) and managed (Blockchain). But if you are the one responsible for planning and implementing systems, this central importance of data means a fundamental change for you.
When you plan classical IT systems, you start with the objectives of the company. Accordingly, you define the requirements. Analysis and design are further carried out on the requirements. Data is an outcome of this process.
Fig 1: Planning for classical IT systems
In Digital systems, however, data drives the system planning. This is again somewhat like like humans. We cannot demand more data from the world. We work out our affairs in such a way that we can manage with whatever data the world gives us. And we have become very good at extracting as much meaning from that data as possible.
So the new paradigm will be:
Fig 2: Planning for Digital systems
As we can see, we are now thrown in a more uncertain and complex world. The existing data indicates possibilities of what can be done. These possibilities have to be mapped on the organizational objectives to decide the Digital Transformation plan.
There are more complexities in the Digital paradigm that shown in the diagram. But for the purpose of this article I would like to present a simple view, leaving the complexities for a future and longer article.
This immediately leads us to the four most important points that can help us to get started with AI. This framework does not consider AI in isolation, but the whole Digital Transformation.
The four hints are:
1.    The data that you have: Identify all the data that your company owns. The data can be put into three major buckets:
  • Structured Data: This is the easiest data to identify. It will be found in all the IT systems that you have implemented so far. Also look for the countless excel sheets that your employees have created.
  • Unstructured data: This will be typically text that has no fixed format. Emails, proposals, invoices, challans, vouchers and so on. Hint- even if you might have some of this data in your IT system, there may be ‘left-over’ data. For example, while some part of an invoice is entered in your AP system, there may be some that is not entered. This left-over might contain interesting possibilities.
  • Dark data: This is that part of your data that you never really thought about. In fact, it may not be being captured today. Photos that can take, vibrations that you can record, video that you can capture – these possibilities are endless. No wonder that IBM put the volume of dark data at 80% of all data.
2.    Objectives of your company: There is a whole load of literature on how to identify objectives of your organization and I am definitely not qualified to comment on it, but broadly they will fall in two buckets:
  • Aspirations: what you would like to happen – improvements, new initiatives, lead over competitors and so on.
  • Pain points: what you would like to remove – delays, leakages, inefficiencies.
There will be various areas of your business that will have their own objectives – customer experience, process efficiency, employee satisfaction, innovation, leadership and so on. I will write no more – you are the best judge.
3.    Applications (or Possibilities): The applications of data are of course endless, but since we are developing a framework, let’s again put them in the biggest buckets:
  • Automation: Essentially replacing human efforts by machine. This not only saves cost, but improves accuracy and speed. In most cases, human effort can be diverted to higher cognitive tasks, giving further advantage to the organization.
  • Analytics: Again lots of material is available on this topic, generally categorized in four types:
                    i.    Descriptive
                    ii.    Prescriptive
                    iii.    Diagnostic
                   iv.    Predictive
The most valuable use of analytics is in decision support, for which it has to be combined with Knowledge (see below).
  • Knowledge: The third and often ignored use of data, is also the most difficult to achieve. Combined with automation and analytics this can give rise to spectacular applications. The currently popular use case of chatbots is an example of the Knowledge possibility.
4.    Existing systems: The fourth hint for getting started with AI is studying your existing IT systems. Most of the above possibilities give best results when interfaced with one of the existing system.
I will now try and put these four things in the framework together with help of a simple example.
Let’s say you have an invoice management module in your existing Accounts Payable (AP) system. The scanned copies of incoming invoices are entered in the system by your employees. These scanned documents are the unstructured data.
The possibility this data presents is that of process automation, along with many others. Now, your company has a defined objective of improving process efficiency, and automation of invoice management fits well with that. Since you already have a AP system in place, the forth criterion is also met.
It seems that automation of invoice management is the right problem statement for your company to get started with AI. There will be a few startups and experienced companies who will be able to help you to get started.
The article was first published on the author’s linkedin pulse page, its been re-published here with the author’s permission. 
Author
Devesh Rajadhyax

Founder and CEO, Cere Labs, AI, Machine Learning, Deep Learning

Why should standalone Hospitals in India focus on IT enabled productivity by Tirupathi Karthik, @TirupathiKarthi CEO at @NapierHealthit


Fresh out of HIMSS India’s inaugural Digital Healthcare Summit, (2015) in Gurgaon, I lamented over the state of healthcare IT in the country. At the time, we were showcasing our hospital information system and launching our telehealth and patient referral management solutions. I should have been proud to be a part of the innovation on display at the event, and understandably so. But what struck me harder than pride at the event and left me with a lingering sense of disappointment was something else. And that was just how far some parts of India lagged behind the rest of the developed world in terms of healthcare delivery and quality.

Napier Healthcare is a global company headquartered in Singapore, where it manages the development of technologies to world-class standards. My other point of reference is the US. So whenever I consider the industry in India, I am invariably piqued by its difference from the industry in Singapore and the US. Especially the US, since it is a democracy like India and has similar health problems on a large scale.

In the time that has passed since HIMSS India, I have thought through some options that the Indian healthcare should seriously consider moving forward. 
EMR Enforced By Law

The most obvious difference I see between India and the US is in their standards and certification environments. We do business in the US and have to be certain that our solutions meet that market’s most stringent regulatory and certification requirements. They include HIPAA and a few others, but more significantly in the case of hospital information systems, the Meaningful Use Stage 2 (MU2) compliance certification. These certifications create significant entry barriers for non-serious players, and make certain that healthcare IT (HIT) quality is maintained in the market.

The policy framework in the US sees to it that EHRs (Electronic Health Records) are sold with certain features that ensure nearly zero medical errors, well-supported transitions of care and ultimately higher quality care delivery.
India has been working on a national EHR standard since 2013, when the Ministry of Health & Family Welfare (MoH&FW) announced its first set of requirements. The MoH&FW has subsequently made continual enhancements of this set of standards. Translating policy intent into effective outcomes still remains a distant goal in most states as software vendors have an option, not a mandate, to comply with this EHR standard.
I must admit, though, that for a large country such as India, executing that would take quite a few years. Even then it would most likely reach only those under some form of formal insurance, and that too only in the metros and some larger cities. As a result, the majority of the population in the semi-urban and rural sector would be excluded from this. 

Automation to Level the Playing Field

The World Bank tells us that private hospitals account for 67 percent of total healthcare expenditure in India. World Bank numbers also tell us that in 2014, citizens paid for 89.2 percent of their healthcare expenses out of their own pockets[1]. These figures have been rising constantly since 1995, and they clearly show two major trends.
One is that private healthcare is enjoying explosive growth in India, with larger private healthcare providers, such as Fortis and Apollo, gaining the lion’s share of the market, and smaller private hospitals being edged out of reckoning slowly but surely. The other is that healthcare is becoming an increasingly heavier burden on Indian citizens. And on poorer households in India, that only drives them deeper into poverty.
My recommendation is for < 100 bedded hospitals to focus on quality rather than volume, and to leverage HIT and automation in their efforts. Automation helps  improve patient-care coordination and ensure the consistency of patient care across facilities, and foster patients’ (and their families’) engagement in their own care. This all adds up to better care and lower costs of delivery for hospitals, and better health outcomes for patients. Today most healthcare providers think of Billing and Inventory as the key areas for automation to the exclusion of everything else. This myopic vision leaves a lot of value gaps that goes un-leveraged and un-monetised.
From the industry standpoint, smaller private healthcare providers who leverage HIT effectively ensure their survival and success, help bring about a more competitive provider market, and ultimately offer better quality care at lower costs.

Get Over Short Termism

Short-term thinking is holding back progress in the healthcare industry and preventing innovation among HIT vendors. Our studies have shown that hospitals  seldom spend more than 0.5 percent of their revenue on IT. Mostly they tend to source customized software solutions from small time players and the mindset seems to be—“cheap is good but free is better.”
Clearly, they do not see IT as a competitive differentiator that can help reduce cost and improve productivity. Small time IT players seldom invest in R&D and rarely provide yearly updates and upgrades. This means that hospitals need to re-implement every time they need to do a technology upgrade. But instead of seeing the potential loss of patient relationships and revenue opportunities that comes with every implementation, many hospitals stay fixated on just how cheap it is to get a new solution every time.

Compared to a global average of 2-2.5 percent of investments by hospitals, or 6-15 percent by other sectors in India itself,  hospitals are far behind their global peers in recognizing the value of good software. The impulse is always to invest for the quick ROI. For example, instead of investment in IT, many CEOs traditionally have wanted to invest in CT Scan or other equipment, which can generate revenue from the following morning itself.

By extension, this approach of managing the affairs of the hospital stifles innovation among IT vendors and limits their ability to invest in R&D for creating innovative IT solutions.
I strongly urge the healthcare leaders to change their mindset and start looking at generating productivity gains by setting up lean and mean operations. The skilled work force is increasingly hard to come by. Hospitals need such critical resources like Doctors and Nurses in abundance to support the opening of newer facilities and not having them will limit growth, like one of our customers in India is realizing very quickly. With abundant money supply Hospitals can easily raise capital today but not having good physicians and nurses will limit their growth for sure.
The only way to achieve sustainable growth is to focus on enhancing productivity rather than just adding to the labor force alone. And quite simply the most effective approach to enhancing productivity at any organization incorporates the innovative use of good technology.
So my key message here goes directly to senior executives of healthcare facilities:   is to view IT as a competitive differentiator rather than as a cost management tool. And recognize that the right software and other tools are essential to making those gains and sustaining your business growth. Continually benchmark your practices to hospitals globally and not just with peers locally.

Finally, Insurance is coming

Increasingly top hospitals are becoming aware that the Insurance reimbursements are a significant portion of their revenue and rising every year. DRG classifications and reporting are going to become commonplace as Insurer’s seek to reduce their cost by paying for “packages” rather than individual services. This means that Hospitals that won’t or can’t respond to the Insurer’s will be left to address private-pay market that will shrink slowly but surely. If one studies the evolution of the US system you will find a strong parallel to the trends in the Indian healthcare system. This will be an existential question for providers.

Like they say “the best way to predict the future is to invent it”. 


Conclusion

  • Healthcare providers need to implement software and other tools with a view to generate productivity gains – not just to generate bills
  • View IT as a competitive differentiator rather than as a cost management tool

The author is the CEO of Napier Healthcare, a Singapore based software provider of technologies such as HIS, EMR, Portals and revenue generating solutions such a Referral management and CRM. He has personally witnessed smaller Singapore healthcare providers with ~100 beds overcome manpower crunch by using technology.

The Article was first published on Mr. Tirupathi Karthik’s LinkedIn Pulse blog, here, and has been republished with the author’s permission.

Author
Tirupathi Karthik

A leader in the Healthcare IT space, Tirupathi Karthik has extensive business leadership experience across Asia, the Middle East and USA, particularly in the enterprise software space. He is a passionate advocate for the innovative use of technology that turns IT investments into competitive differentiators for their stakeholders rather than using IT as a pure cost containment initiative.

In various hospital implementations, he has been championing the use of Mobility as a pervasive information delivery channel. His vision led to the use of themFirst approach with the infusion of HTML5 and Apple’s mobility products across the Napier platform. Napier’s leadership in the global marketplace continues to gather momentum on the back of one of the most modern implementations of such a technology stack.

As an Eldercare thought leader, he has been driving productivity agendas for aged care models globally and seen to the expansion of Napier’s product vision to include elderly care services delivery. Applying technology-enabled solutions for senior care providers offering nursing home, home care and activity-centre services, Napier today enables productivity and improved quality of care.

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