#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.


Read more »

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
Read more »

#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. 

Read more »

#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.

Read more »

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.

Read more »

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. 

Read more »

#Blockchain in Healthcare: Will it or won't it survive? By Tirupathi Karthik, @TirupathiKarthi




What is Blockchain

Blockchain offers a permanent record of online transactions. Transactions are deemed as a “Block” and a ledger binds them in a “chain” thus earning its moniker “Blockchain”. Each transaction is validated and stored by a network participant based on rules but sans a governing central authority. Information can neither be modified nor copied or deleted.

Every transaction has a time and date stamp, offering a trusted transaction history and allowing verification of such records. Since the information is encrypted, the only way to access the blockchain is with a passcode. This shared ledger system makes Blockchain rather secure. Given this, Blockchain is gaining new use cases for applications that require trusted and immutable data.

Read more »

Containing Health Care Cost, What is our role as a Physician? by Dr. Chandrika Kambam @Ckambam




Indian health care is at an inflection point. Today governments’ spending on healthcare needs is one of the lowest amongst the Developing countries [1]. India spends about 5% of the total expenditure on Health which is around 1.7% of the GDP. Public healthcare growth has slowed down over years. In 1998 about 43% of population was served by Public Hospitals and today only 30% use the Public health care system. [2] That means almost 70% of the health care needs are serviced by Private players, trust hospitals and non-profit institutions. This has led to the rapid growth of Private players who are growing at the rate of CAGR 16.5% year on year [3]. The costs of procedures or hospitalization has increased anywhere from 83% to 263% in 10 yrs. i.e. 2004 to 2014. There is also a wide variation of the cost for the same procedure in different hospitals [4]. It is also noted that 86% of rural Indian patients and 82% of urban Indian patients do not have access to any form of employer-provided or state-funded insurance.

Read more »

Software Product For Hospital Industry by Girish Koppar @KopparGirish



Before we talk about software product for hospital industry lets understand how the Hospitals are broadly classified

- Based on the legal entity ( Private , Trust or Corporate)
- Based on specialty ( Super specialty, Multi-specialty, Single specialty)
- Based on bed strength ( Larger hospitals and Nursing Homes)

Read more »

Electronic Health Record System from the Perspective of Data Privacy by Dr. SB Bhattacharyya @sbbhattacharyya

Electronic health record systems handle health-related ultra-sensitive data of a person throughout his life, along with all personal information that accurately identifies him. This makes it imperative to protect the data from cyber-threats and consequent untold damages. This article discusses the various issues involved and the different mitigation methods.

During the course of any clinical encounter a person discloses ultra-sensitive health related information to his provider to enable the latter to address his health-related problems better, faster, and hopefully, cheaper. Information that he would otherwise rather keep well under wraps. Ethics demands all providers treat all information that their patients disclose to them with the greatest of care and keep them secreted away from everyone, even the spouse, unless explicitly released from this obligation by the patient. The confidentiality of the private information needs to be maintained at the highest possible levels of security by medical professionals at all times—unless there are extenuating circumstances to disclose them, like for the public good, compliance to the law, etc.

Read more »

A Data Scientist’s Experience in Decoding Chest Imaging by Vidya MS



The Chest Imaging Update 2018 held by the Narayana Health group, brought together over 150 radiologists, pulmonologists and doctors gathered to update and improve their knowledge in the reporting of Chest Imaging, both X-ray and CT. As a data scientist with keen interest in medical imaging, my aim was to get an inside look into the daily practice of medical professionals in detection and diagnosis of pulmonary diseases.
Read more »

PregBuddy’s year with Google Launchpad by Sivareena S. L. @SarikaSivareena




We’re all aware about the Google Launchpad accelerator which selects pre-series A startups across the globe every year to assist them scale their business. Along with this, Google Launchpad has few more offerings where they have extremely well structured programs for various stages of startups. Pregbuddy has benefited from couple of these programs as we grew our product.

Read more »

Almighty Data or Hype? By INDERJITH DAVALUR @INDERDAVALUR

DIGITAL TRANSFORMATION AND THE PLACE FOR DATA

Mea Culpa, I am one of those who is guilty of getting on and staying on the Big Data wagon for the wrong reasons. “Data is the new oil” is an oft-repeated phrase. I am about to commit a “virtual” suicide by proclaiming that it is not so. Data has its place and it is not at the top of the digital food chain. I feel that we have crowned the half-naked prince, Emperor in haste.

For the sake of clarity, when I say data, I will be referring to digital data throughout this piece. Data is a by-product of any activity. Therefore, creating data is as natural as breathing. So we have data. A lot of data. So what? Accumulating data, structuring it, storing it, analyzing it are a natural progression from that point onwards. How and what we do with the data is more important. Software. 

The magic that is software, to me, is more transfixing. Consider the prospect of a language written in a semantic that is alien to our natural human language. A cryptic command, logic, condition, trigger – anything at all – that is magically read, understood and acted upon by silicon. Hardware that contains baked-in code that can parse and carry out complex instructions at blazing speeds. Pieces of such chips soldered on a board and communicating through ‘roadways’ of circuits laid out on a board. The miracle of hardware coupled with the magic that is software is what gets my adrenalin pumping. How can such a marvel not be exciting?

Even the awesomeness of hardware pales in comparison to software. Hardware is more or less static. It is confined to physical and functional dimensions. Software, however, is supreme. It can use the same hardware (with some limitations of course) and carry out simple tasks, entertain with games, or perform wildly complex calculations at very very high rates of speed, accurately all the time. And it can do this million million times with alacrity. This is just the beginning of what software can do. But wait, there’s more!

Consider intelligence in software. It suddenly becomes a living, breathing, dynamic being. Almost. Software can learn and teach itself. Crunching data and spitting out patterns and actionable analysis suddenly becomes mundane, banal almost pedestrian. No. I am not against data or big data. By itself, big data is just that. A monstrosity. Sometimes, big data actually gets in the way. Misleads us in making decisions quickly. Software breathes life into data. 

Take any software language or tool. Examine it. Study its flow, the eloquence, the nuance and its brilliance. Brevity in software coding is revered by programming perfectionists. There is elegance in a well-written piece of code that executes beautifully, perfectly, every time. Anyone that can find literary melody in Shakespeare or Milton can certainly begin to enjoy the harmony in a beautifully crafted software application code. So, my appeal goes out to all those who are worshipping big data to take a moment to reflect upon the joy that software brings to our daily lives. After all, the future is software!

Author
Inder Davalur
Inderjith Davalur is a healthcare technology specialist, speaker, writer and utopian dreamer.
Inder works with hospitals committed to transforming the healthcare paradigm with the aid of new innovative technologies. His primary area of interest lies in using data analytics and technologies such as Deep Learning to shift the current physician-driven healthcare model to a patient-driven market dynamic.
Inder focuses on the manifold ways in which data crunching and machine learning can lead to better diagnoses that can not only be made at the time of illness, but predicted way before any symptoms surface. The path ahead in the sector, he believes, lies in the deployment of evolving technologies that immensely influence both diagnostic and therapeutic aspects of healthcare, delivering real patient-driven, data-enabled, informed healthcare.
Inder currently works as the Group CIO at KIMS Hospitals Private Limited, Hyderabad and has previously assumed leadership roles at leading hospitals and companies, in India and the United States of America.
Read more »

Simplifying Health Economics by Dr. Karan Sharma

After hearing about India's New Health Insurance Program, I thought it is good idea to share about Health Economics, so here I am

Health economics is a branch of economics concerned with issues related to efficiency, effectiveness, value and behavior in the production and consumption of health and healthcare. 

Read more »

Some perceived shortfalls in the proposed Indian National Health Stack by Dr. Pramod Jacob

There is ongoing work in India for a Nationwide Information Technology platform, that will support and facilitate the deployment of the Ayushman Bharat program, which is called the “National Health Stack”, the objective of which is to help achieve Continuum of Care across Primary, Secondary and Tertiary care for each of its citizens and facilitate payment for the care.


A draft of the National Health Stack (NHS) strategy and approach was put out in July 2018 for feedback and comments till July 31, following which no final draft has been published in the public domain. Hence the shortfalls brought out in this write up are based on the July 2018 draft and so these are perceived shortfalls, because the final version may have addressed these concerns. If so, request that the final document be published in the public domain. http://niti.gov.in/writereaddata/files/document_publication/NHS-Strategy-and-Approach-Document-for-consultation.pdf  

Read more »

Universal Healthcare: How do we get there? by Ritesh Dogra @ritesh_medium

There is undoubtedly a clear argument for Universal healthcare. The question still looming large is “How do we get there”


Angus Deaton, a well renowned economist, explains that while there is a correlation between higher income and better life expectancy, this is not the only factor. There are means to ensure great health at less cost and equally spending large sum with no purpose, America being one case in point. While earlier any spending on healthcare was dubbed as social overhead, it is no longer so – there is enough evidence to prove that spending on healthcare speeds growth of the nation.

Read more »

Timeline: The History of the EMR/EHR by David Rice @bigdatadavid13



Much of the conversation around healthcare technology is centered on where new developments are taking us. But as the age old adage goes, you can’t know where you’re going until you know where you’ve been.
Read more »

A PhD Researcher's QnA on #BigDataAnalytics (BDA) with a Healthcare CIO by Inder Davalur, @INDERDAVALUR & Nishita Mehta



Q1. Nishita Mehta: What is data’s role in healthcare & how do you see it influencing future health sector growth in India?

A. Inder Davalur: 

Big Data Analytics (BDA) will have a huge role in healthcare. Healthcare has been a latecomer to using IT as a tool but the future looks good. AI and its children – ML, IoT, and M2M are excellent candidates for advancing technology in healthcare. There is a real potential for technology to advance what I have termed “Connected Continuum of Care” in one of my blogs. This means that with wearables and other Internet of Healthcare Things (IoHT), creating a biome where the patient and doctor/hospital are always connected would become a reality. Always-on Internet is the future and extending that to healthcare is a natural progression. With the price of Internet in India being one of the lowest in the world, we will be in an excellent position to incorporate technology in advancing healthcare delivery.   

Read more »

How Mobile Will Transform Primary Healthcare Access in India by Prasad Kompalli, @pkompalli ( mfine @mfinecare )



A few days ago, we came across a very interesting albeit a rare case where a mother wanted to consult a paediatrician. Under a few minutes, she was able to have an online consultation with one of the top paediatricians in Bangalore, who immediately prescribed the required treatment for her child as the  symptoms were severe. At this point the patient informed the doctor that she was on a moving train and travelling towards Bangalore but needed the assistance urgently and was glad to have spoken to him. The doctor meanwhile was totally taken aback. Quickly recovering, he felt a deep appreciation for technology and its ability to empower people and help them access essential services at the hour of need.

Read more »

I & L to #AI & #ML in Healthcare by Jyoti Sahai, @jyotisahai

Have you ever wondered why if confronted with any illness symptoms that appear even a bit abnormal, we prefer to consult with a doctor in a large hospital only, even though a more competent doctor may have a clinic next door itself.

Read more »



POPULAR POSTS

Popular Posts