Tag: BigData

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.   


Q2. Nishita Mehta: What are the unique challenges of working with clinical data? 

A. Inder Davalur: 

Doctors. Well, the challenge lies with the fact that most clinical data is unstructured. Doctors and hospitals are notorious for NOT wanting to follow standards when it comes to coding diagnoses and treatment. Adoption of DSM, Snomed, ICD codes is very spotty. Physicians complain about the inconvenience in the classifications and prefer to use free text in writing their diagnoses and treatment protocols. This creates a credibility gap in how clinical data can be meaningfully classified and analyzed for any useful prediction or AI driven protocols. EMR applications in India struggle with the similar challenges with physicians disagreeing on a set of standards in capturing and documenting clinical data. 

Q3. Nishita Mehta: Healthcare seems to be moving from the use of structured data to unstructured data. What is the difference between them when it comes to clinical utility & improving patient outcomes?

A. Inder Davalur:  

Healthcare has always suffered from a lack of structured data. Unstructured data creates several challenges in a software trying to classify the same diagnosis written with slight variations. The same fate awaits treatment plans. If medical coding (DSM, ICD etc.) is followed, it will enable any analytics software to make sense of the data and provide useful insights. With AI, structured data is still king. Predictability of an outcome for a set of patient symptoms, medications, prior history, genetic propensity, lifestyle habits would have a high accuracy 

Q4. Nishita Mehta:What do you think does a hospital need to implement Big Data solutions, i.e. Big Data Analytics Capabilities in terms of infrastructure and personnel requirement?

A. Inder Davalur:  

One of the major challenges a CIO or an IT head faces in a hospital is the lack of budget allocation for anything beyond the basic networking, computing and storage needs. Hospitals do not see the value in the data they currently possess most likely because they are more empirically driven rather than evidence driven. What this means is that hospitals and by nature the doctors who hold a sway over management decisions are more comfortable with their own decades of experience over some hotshot CIO trying to promote the idea of data mining and predictive value of patient outcomes based on past data. There is also a severe shortage of technology-rich personnel in hospitals due to the dull routine of maintenance of existing hardware and software rather than experimenting with new technology. The pay structure for IT personnel in hospitals is also woefully poor in comparison to the technology companies. All these factors combine to deter anyone who is driven to create in hospitals a digital core

Q5. Nishita Mehta: While Big Data can generate a plethora of interesting patterns or hypotheses, there is still a need of experts to analyze the results to confirm whether they make sense or merit further inquiry. Would you like to comment on this?

A. Inder Davalur:  

Absolutely. Right now, there is a paucity of people with skills to interpret and recommend action plans once an organization implements any sort of analytics software. Unlike other verticals, healthcare is lagging far behind in its focus on data interpretation and application in its business model. It might be a whole decade before hospitals wake up to the reality of meaningful interpretation of data and building an action plan around it


Q6. Nishita Mehta: What are the major drivers of Big Data Analytics in healthcare in India?

A. Inder Davalur:  

Have not seen much evidence of it. Perhaps some hospitals have ventured into some basic AI driven applications in specific areas such as pharmacy sales or patient wait times. Other than that, BDA is yet to catch up.

Q7. Nishita Mehta: What are the key benefits Indian hospitals will draw from implementation of Big Data Analytics? 

A. Inder Davalur:  

First and foremost, hospitals will get to see for themselves how poorly structured their data is. BDA for Indian hospitals can cover a better management of the following: 
  1. Sponsored
  2. Accounts Receivables
  3. Professional Fees
  4. Disposables and Consumables
  5. Pharmacy – Generic vs brand name consumption
  6. Targeted marketing
  7. Continuum of Care post-discharge
  8. Predictability of illness propensity from regular Health Check ups
  9. Results-oriented tasking for better output from employees

Besides these areas, hospitals can contribute tremendously to public health issues by sharing anonymized patient data with the State Health Department which can then study outbreaks and lifestyle disease patters in the general public. 

Q8. Nishita Mehta: How does Big Data Analytics help better decision-making & building disease understanding?

A. Inder Davalur:  

One of the most ignored areas is a deeper dive into results from investigations. Empirically speaking, the values considered “normal range” are never questioned. If a better study is conducted, what is normal for one cohort may not be so for another cohort. As an academic exercise, I had a simple deep analysis done to study the correlation between borderline values of lipid profile and any other element from a blood test. The result was a high (>70%) correlation between borderline lipid profile values and an elevated monocytes count. It turned out that among those who fell in this group, nearly 78% of them were later admitted for some coronary complication. The medical reason is that the monocyte levels are elevated when there is presence of a heart disease. Every one of these patient was merely getting a Health Check. Imagine if hospitals did such studies on a multitude of investigations routinely conducted for patients and conducted regular follow ups as a preventive measure

Q9. Nishita Mehta: One of the biggest concerns in healthcare is the rising costs. What potential solutions does Big Data offer for this problem in Indian context?

A. Inder Davalur:  

India’s population is now facing more mortalities from lifestyle diseases – Non Communicable Diseases (NCD) as opposed to communicable diseases. There is a great potential to flip the business model of the healthcare industry to go from disease management to health management. I have written blogs on this topic. The premise is very simple. Make it more profitable for hospitals to keep the public healthy than to treat them. If the payment structure is modified to increase the prices for health checkups and promoting healthy prophylactic therapy methods as opposed to coronary by-pass surgeries, it could completely change the paradigm. These prices can be graded based on age. All old age related treatments can receive higher prices; while treatments like a heart surgery for a 40-year old can be less. At the same time, therapeutic treatments for younger population geared for promoting good health can receive higher prices. A larger healthy population means a larger market for the hospitals. This ensures that the hospitals have a higher incentive to make the healthy population larger

Q10. Nishita Mehta: What would you highlight as being the major challenges today in developing & actually implementing Big Data Analytics capabilities to truly extract meaningful insights?

A. Inder Davalur:  

An urgent awareness creation among promoters and owners of hospitals of the benefits of investing in the technical hardware and personnel resources to build and maintain a BDA infrastructure. Without that awareness, IT costs are always seen as a sunken wasteful expenditure as opposed to an investment. There is nothing else lacking in this respect.

Q11. Nishita Mehta: Do most doctors now have a checklist for what they should be doing with patients with certain conditions? How does Big Data solution change what they are doing currently?

A. Inder Davalur:  

Hard to predict. Most clinical pathways and treatment protocols are traditionally empirically driven. It is hard to imagine a medical community to take notice of what BDA might reveal and radically change their protocols. That said, things have changed – take robotic surgery – and there is hope and a high degree of probability that medicine might be “data-powered” (my phrase over the more commonly used data-driven) offering the physician to choose to use such data-powered results wherever she finds it viable or desirables

Q12. Nishita Mehta: How do hospitals need to adapt to embrace the full potential of data-driven innovation?

A. Inder Davalur:  

Promoters and owners having a greater understanding of the power of data

Q13. Nishita Mehta: How important do you think Big Data Management & Analytics is right now to enhance healthcare in India?

A. Inder Davalur:  

Tremendously. With the technical resources at its disposal, India would be imprudent not to take full advantage of the benefits of BDA. Population health data is one of the most ignored among developing nations. India would do extremely well to develop and use BDA for advancing population health

Q14. Nishita Mehta: What do you see as the main emerging opportunities for hospitals from greater adoption of Big Data Analytics?

A. Inder Davalur:  

Connected Continuum of Care (a phrase I first used in a blog) is a concept of keeping the patient engaged post treatment and post discharge through the use of wearables and IoHTs (Internet of Healthcare Things). This ensures that hospitals are not merely agents in episodic encounters and instead become agents of well-being. BDA will help provide the big picture in the overall health and well-being of the population it serves

Q15. Nishita Mehta: What are some of the biggest challenges facing the healthcare industry in terms of its ability to use Big Data to improve healthcare outcomes?

A. Inder Davalur:  

A better understanding and incentive to invest in the infrastructure is all it takes. Once that happens, India is best equipped to leverage from its large technology-aware population. At the hospital level, BDA could help establish a new approach to purely outcomes-driven pricing structure and treatment protocols that would be data-powered. 

Q16. Nishita Mehta: Would you like to share additional insights on the topic, which I might have missed?

A. Inder Davalur:  

Public-Private-Partnerships with educational institutions and hospitals would also be beneficial. There is going to be a severe shortage of technical resources who are trained in AI and BDA by 2020. If the government partnered with colleges to promote courses and training in AI and BDA India could be the largest supplier of technical talent to the world. If hospitals also partnered with the government to share health data, the state of overall population health will rise and costs will come down.

The article was first published on Mr. Inder Davalur’s LinkedIn Pulse page. The blog was Mr. Inder’s answers to Ms. Nishita Mehta’s Survey published on the HCITExpert Blog earlier, here. I would like to thank both the Author’s for sharing their insights via the HCITExperts Blog. 
Team @HCITExperts [Updated: 03 rd Sep 2018]
Authors
Nishita Mehta

Ph.D. Scholar at SYMBIOSIS INTERNATIONAL UNIVERSITY

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.

SURVEY: The use of Big Data Analytics (BDA) for better healthcare delivery in India by Nishita Mehta

This survey tries to understand the state of healthcare data management in hospitals in India and the use of Big Data Analytics (BDA) for better healthcare delivery


The results of this survey will help obtain insights in how the hospitals can make use of technological developments for improving care delivery. 


The survey has been created keeping in mind the expertise of respondents, hence it has been divided into two categories: 

(a) User: hospital personnel 
(b) Solution provider/ healthcare technology experts. 

The survey has been distributed across two parts, each would take around 10 mins to answer. The responses to this survey will remain anonymous and will only be used for research purpose. 

I personally thank you for each minute you invested in this research.

(a) User: Hospital Personnel
Part I: https://goo.gl/forms/AS52RYFA4VDP9ct42
Part II: https://goo.gl/forms/ICXKGd19exvuMito2

(b) Solution Provider/ Healthcare Technology Experts
Part I: https://goo.gl/forms/WblNKZgRkq8PGAXs2
Part II: https://goo.gl/forms/WhUUIvfQCuvciwEG2

Author
Nishita Mehta

Ph.D. Scholar at SYMBIOSIS INTERNATIONAL UNIVERSITY

Case Study: Efficiently Converting Healthcare Data into Information and Intelligence by @uddenfeldt


This article was published by Mats Uddenfeldt, on his LinkedIn Pulse page [ https://www.linkedin.com/pulse/case-study-efficiently-converting-healthcare-data-mats-uddenfeldt ]. The article is republished here with the author’s permission.
Valence Health provides healthcare providers with customized solutions for value based care, helping them better manage their patient populations and accept financial responsibility for the quality of the care they provide. The company offers advisory services, health plan services and a suite of population health technology software as a service (SaaS) products that help their clients transition from a transaction-based approach to a value-based approach to healthcare. Headquartered in Chicago, Valence Health serves 85,000 physicians and 135 hospitals, helping them manage the health of 20 million patients nationwide.


The Challenge

The company’s rapid client growth and the increasing volume of data required to keep up with that growth were straining its existing technology infrastructure. “Our services have a voracious appetite for data. We use that data to inform decisions about improving both healthcare outcomes and operational processes. We knew if we continued to grow, we couldn’t sustain this,” explains Dan Blake, Valence Health Chief Technology Officer. “We had outgrown our Extract Transform and Load (ETL) infrastructure and knew we had to replace it.”
Valence Health looked at various alternative technologies. “We looked at large monolithic technologies like Informatica and point solutions like Syncsort but they did not give us the robustness and flexibility we needed in the long run,” says Blake. “We wanted something very open that would give us the flexibility to choose where to make investments over time. We were drawn to Hadoop and the capabilities those tools provided.”

MapR Solution

Valence Health is using the MapR Converged Data Platform [ https://www.mapr.com/products/mapr-converged-data-platform ] to build a data lake that is the company’s main data repository. The company consumes 3,000 inbound data feeds with 45 different types of data including lab test results, patient vitals, prescriptions, immunizations, pharmacy benefits, claims and payment, and claims from doctors and hospitals.
“NFS was a very important feature for data ingestion. It is making our migration much easier,” says Blake. In the short term, Valence is transferring data back to the SQL server database as their portal and analytics expect that format. Once they get through the ETL transformation, they plan to transition from SQL to an HBase solution.
    “We chose MapR [ https://www.mapr.com/why-hadoop/why-mapr ] because they were the easiest company to work with. They were the most receptive and answered questions quickly, honestly and efficiently. On the technology side, we’ve been very impressed with the overall ability to implement and integrate sequential data transformation.” Dan Blake, Chief Technology Officer, Valence Health

Benefits

Valence Health is already seeing many benefits from its MapR solution including increased performance, better responsiveness to customers, higher quality data and a flexible platform to sustain their growth over time.

Growth requires new data architecture that can scale the business

Valence Health has been on a steep growth path over the last several years. “In the wake of the ACA’s implementation, more and more healthcare providers and organizations like Consumer Orientated and Operated Plans (CO-Ops) are taking on risk. As a result, we have tripled the size of our business in three years and expect do the same next year,” says Kevin Weinstein, Valence Health Chief Growth Officer. “Every year we’re more than doubling the amount of data we are processing. Having a robust data architecture is integral to our success.”
The company’s growth path is tied to client growth and the growth in the data infrastructure that those clients require. “We have to use technology to scale the business. We have to be able to manage more data with the same amount of people,” says Weinstein.

Performance gains increase customer satisfaction

The reliable and high performance of the MapR Platform enables Valence to be much more responsiveness to their customers. “In the past, if we received a feed with 20 million lab records, it would take 22 hours to process that data,” says Blake. “MapR can cut that cycle time down from 22 hours to 20 minutes [ https://www.mapr.com/company/press-releases/valence-health-dramatically-improves-data-ingestion-performance-and ] . And it’s running on much less hardware.”
“MapR gives us the resource efficiency, speed and flexibility to make a huge difference in customer satisfaction. As soon as the data hits our system it’s pushed all the way through,” he says. “It gives our customers much faster feedback about what’s going on with the population they are trying to manage.”

Flexibility serves customers faster

Valence Health is also now able to answer customer requests that were very difficult to answer in the past. “It allows us to do things we could not do in our old world,” explains Blake. “For example, a customer might call and say: ‘I sent you an incorrect file three months ago and I need you to take that file out.’ That’s not something you can do in a normal ETL system on top of a relational database. It could take 3-4 weeks to get that data deleted,” he says. “But with MapR, that is naturally supported, we can just roll it back and take that file out.”
This ease of administration and maintenance means that the company can focus more resources on their core business. “I can spend less on outsourced resources and instead spend money on adding new features, analytics or visualization capabilities or acquiring new types of data. We can do things that truly matter to our customers,” says Blake.

Enriching the data lake with new data sources enhances data quality

The MapR Platform also makes it much easier for Valence Health to enrich their data lake with new data sources. “It’s not just the volume of data, we’re looking to integrate new types of data like socioeconomic and demographic information, or immunization records. With our old architecture it was painful to do so,” says Blake. “Our data scientists are looking at new sources of data. The data can tell you things you don’t even know about. If we can augment our data, we can build new types of analytics that allow our client and our company to successfully invest in areas we have not been in before.”

Data acquisition and integration capabilities enable differentiated services

Valence Health believes that their new data acquisition and integration capabilities will give them a leg up over their competitors. “Other startups are selling software solutions in spaces we operate in,” explains Blake. “But the hard part is getting the data into the system and into formats where it can be truly useful. Our twenty years of hands-on practical experience in working with provider organizations that have taken on all sorts of risk-arrangements coupled with our effective and efficient infrastructure to get data flowing and keep it flowing is hugely powerful. The data acquisition infrastructure is very important to our ongoing success and to our customers.”

Recommended Reading

Want to learn more about Big Data for the Healthcare industry? Please check out the links below:
  1. How Health Care IT Diagnoses Data Pain Points, CIO Insight – http://www.cioinsight.com/case-studies/how-health-care-it-diagnoses-data-pain-points.html
  2. Stepping Up to the Life Science Storage System Challenge, HPC wire – http://www.hpcwire.com/2015/10/05/stepping-up-to-the-life-science-storage-system-challenge/
  3. Health Care Emerges as Hadoop Use Case, Datanami – http://www.datanami.com/2015/10/08/health-care-emerges-as-hadoop-use-case/
  4. Big Data and Apache Hadoop for Healthcare and Life Sciences, MapR – https://www.mapr.com/solutions/industry/big-data-and-apache-hadoop-healthcare-and-life-sciences
Join our more than 700+ paying customers and discover why MapR [ https://www.mapr.com/ ]  is the clear market leader for production ready Big Data applications by reading about the Top 10 Reasons Customers Choose MapR [ https://www.mapr.com/top-ten-reasons ].
This article was published by Mats Uddenfeldt, on his LinkedIn Pulse page [ https://www.linkedin.com/pulse/case-study-efficiently-converting-healthcare-data-mats-uddenfeldt ] . The article is republished here with the author’s permission.
Author
Mats Uddenfeldt

Big Data Thought Leadership ♦ Enabling As-It-Happens Business

From Mere Health Statistics to Real Health Data by @AtulVB


The Health Statistics play a major role in deciding the Health Policies of Nations, no doubt they provide the insights into the health parameters in question and health statistics such as Rates, Ratio, Incidence, Prevalence and Life Tables are needed to be formed into indicators of progress of the nation in terms of improvement of the factors in consideration.

The question is – is mere statistics enough or is there something more to it?

As we move from Millennium Development Goals (MDGs) to Sustainable Development Goals (SDGs) – this question becomes more imperative!

According to WHO, Health is defined as “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”. 1

Health is also identified the state of “not well”, “ill “or “morbidity” or “sickness”. Further “death”, which is caused by illness or sickness, reflects the condition of Health. Hence measurement of “Mortality” and “Morbidity” reveals of Health condition of a community.2

Statistics definitely provide a macro-level / micro-level understanding of the situation and brings in quantitative pointer to a qualitative, exhaustive and comprehensive activity. Year-on-year the quantitative pointer is kept as the reference. Any deviation from it is representative of the growth or decline of the parameters in consideration.

Such laser sharp focus on quantitative data is the hallmark for Population Census, Surveys and even Market Research – where absolute number, a figure in percentage is the gospel truth.

What about Health / Medical Data of the population? What about the Medical History? What about the Medical Condition – improvement / degradation?

The last point gets easily converted into an explainable number or a percentage as an indicator, but what about the relapse or repeat condition of the same person – it’s again… just a number!  

Limitations of data especially in developing countries are a real concern, as available data is not reliable and post-2015 presents an opportunity to think beyond what data is available so that countries can invest in capacity building to get it.3

Alongside the number comes a lot of health and health-related data, but with the focus being statistics, basic essential health data is obviously missed out.

Non-communicable disease continues to be an important public health problem in India, being responsible for a major proportion of mortality and morbidity. Surveillance of NCDs and their risk factors should also become an integral function of health systems. Evidence based clinical practice and appropriate use of technologies should be promoted at all levels of health care, including tertiary services.4

With the focus on Non-Communicable diseases (NCDs) – it is imperative we focus on the Healthcare & Medical Data for Clinical outcomes and not mere Health Information for Management Systems.

Ageing Population is one of the major concern globally, more so in India. It is now recognized that while both developed and developing countries are experiencing growing proportions of elderly, developing countries currently are ageing faster than developed countries. In India, the proportion of the population aged 60 years and above was 7 per cent in 2009 (88 million) and is expected to increase to 20 per cent (315 million) by the year 2050. 7

As per a study conducted 5, among the most significant findings that emerged was the incompleteness of data on the burdens of access and affordability among elderly populations in India. A major reason for this is that routine health data collection in India is not designed to reflect or characterize pathological progression. Many routine data collection procedures (National Sample Surveys, Census data, or death certificates) in India do not capture pathological progression nor do they disaggregate morbidity and disability outcomes among the elderly. 5

Further research, especially qualitative research, is needed to explore the depth of the problems of the elderly. 6

With a rise in the Ageing Population, it is all the more important to look at the efficacy of the health data collected than mere health statistic data and a Longitudinal Study along with Cross-Sectional Study is needed for an efficient health data repository.

With the advent of new age technology: connected technology, Connected Health has become pervasive and with embedded systems and IoT taking center-stage, it is all the more essential to focus on the Data and not mere Number! And when I say ‘Data’ it is ‘Health Data’.

And last, but not the lease – the necessity for increased clinical / medical research in today’s evidence based approach makes it is all the more important to focus on the fundamental health data collection, collation, transformation and consolidation to begin with leading to Analysis, Research and building the knowledge base for Healthcare / Medical Data Management.

Looking at the underlining need for Health / Healthcare Data, we need to move from just Statistical Data to Meaningful Data – not to mistake ‘Meaningful use of Data’!

The article was first published on Mr. Atul Bengeri’s – LinkedIn Pulse. The article is republished here with the authors’ permission

References
  1. CIGI, TISS, KDI, Post-2015 Development Goals, Targets and Indicators: Indian Perspectives, Mumbai, India / Meeting Report, August, 2012
  2. An Overview of the Burden of Non- Communicable Diseases in India – R Prakash Upadhyay. Iranian J Publ Health, Vol. 41, No.3, 2012, pp.1-8 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3481705/pdf/ijph-41-1.pdf
  3. Health of the Elderly in India: Challenges of Access and Affordability. Subhojit Dey, Devaki Nambiar, J. K. Lakshmi, Kabir Sheikh, and K. Srinath Reddy. National Research Council (US) Panel on Policy Research and Data Needs to Meet the Challenge of Aging in Asia; Smith JP, Majmundar M, editors. Washington (DC): National Academies Press (US); 2012. http://www.ncbi.nlm.nih.gov/books/NBK109208/
  4. Health and Social Problems of the Elderly: A Cross-Sectional Study in Udupi Taluk, Karnataka A Lena, K Ashok, M Padma,1 V Kamath, and A Kamath. Indian J Community Med. 2009 Apr; 34(2): 131–134. doi:  10.4103/0970-0218.51236 PMCID: PMC2781120
  5. Demographics of Population Aging in India. Subaiya, Lekha and Dhananjay W Bansod. 2011. Demographics of Population Ageing in India: Trends and Citation Advice: Differentials, BKPAI Working Paper No. 1, United Nations Population Fund (UNFPA), New Delhi.

Author

Atul Bengeri

Digital Health Influencer & Evangelizing Digital Transformation across verticals, Strategic Planning, Leadership, Program Management, Partnerships / Alliance Management

Zen Clinicals: An Activity & Workflow based solution (1 of 4)

Part 1 of 4:

Recently, during the Gartner Symposium, it was predicted that cognitve platforms would take over a lot of activities. Keeping this future at the back of our minds, its important for the EHRs of today to metamorphosize to the Cognitive Computing platforms of tomorrow, and fast.

With social media, predictive and bigdata analytics become more central to the discussions; the current EHR systems are woefully lagging behind in the ability to catchup with the technology of today. Newer EHRs or healthcare based platforms too are lagging behind in the adoption of these technologies. Most often have observed the similar functionality being rolled out on newer technology.

In most cases I have found the solutions to be non-workflow, i.e., non-BPM, enabled solutions that have the inability to adapt to a new healthcare scenario faced by the solution in the next implementation. With the advent of the Software as a service platforms and the proliferation of the cloud based services in other industries, healthcare solutions have generally gone for the traditional (non-BPM based) approach to delivering a solution. 

So, One needs to then ask, “What should the future of a patient health record (PHR) or the electronic health record look like?” 

Will we really see the advent of SMART solutions in healthcare, or are the changes to difficult too incorporate into the solution because that’s how the solutions had been developed?

In this series I present some thoughts on what should the solutions of today morph into to meet the needs of the users and hopefully make it more user-friendly in the process.

And to develop the solution, Zen Clinicals (this is a fictitious name of a solution any resemblance is purely a coincidence), we need to answer a few questions upfront:

  • who do you build it for? 
  • Who are the actors? 
  • And what do they need? 
  • What are their activities? 
  • What do they do daily?
“User Experience strategy lies at the intersection of UX design and business strategy. It is practice that, when done empirically, provides a much better chance of successful digital product than just crossing your fingers, designing some wireframes, and then writing a bunch of code”

UX Strategy by Jamie Levy (O’Reilly)



Lets explore the actors in a small clinic. And list out the answers to the questions.

The Actors: 

  • Physician
  • Nurse
  • Billing Person
  • Customer Service Person
  • Customer (Patient)

The Activities:

Its is important to understand the way each of these people work. Its a mix of their training (the way each of these people perform their tasks) and the requirements of their roles. 

We define an Activity as 

Lets take them up one by one. 


The Physician Activities:

The Physician is the focal point from the operations perspective. What does she need? 

For this lets consider Dr. Jane’s typical day. She gets ready and heads out for work. Reaches her office, marks her attendance at the biometric scanner. She then heads out to her office and logs into her system.

On the way to her office, the nurse Jenny informs her of the number of patients that are scheduled for the day and the number of patients that have already arrived.

Dr. Jane, checks out her emails before starting to see her patients. She then heads over to the system to view the list of patients that are waiting to see her. Before she calls on the first patient, she reviews the patient records from the previous visit to bring herself upto the current status of the patient.

She calls her first patient.

The Nurse activities: 

The nurse is a master tactician who works as the floor manager in the clinic. She handles the multiple schedules, that of the physician and the patient. While the doctor focuses on treating the patient, the nurse handles not only the administrative work related to that patient, but also the clinical preparations that a patient might need pre or post seeing the doctor. 

Nurse Jenny, started her day by arriving at the clinic about half and hour before the clinic was to open. She checks up on the list of appointments scheduled for the day and orders the relevant medical records as per the way the patients are scheduled to arrive for their appointments.

Dr. Jane arrives at the clinic and Nurse Jenny informs the doctor about her appointments for the day and any other Administrative aspects that needs the Doctors attention.

Meantime, the first patient scheduled for the appointment, calls the nurse to inform if the doctor will be able to consult her via video conference. Nurse Jenny schedules the video conference for the patient with Dr. Jane.

The Billing Person’s Activities: 

The billing person arrives at the clinic and decides to check on the status of the outstanding dues, the patient ledger of the patients visiting the clinic today to review their insurance details and the plan of processing first time patient insurance details.

The billing person also completes the coding activity for the bills that are to be processed. The billing person sends the billing statement to the uninsured patients who owe a balance for their visit. The billing person also issues the billing statement to the insured patients once the insurance company processes their claim.

The Customer Service Person

Elaine starts from her home to the clinic and is immidiately alerted by the customer relationship management system regarding the various appointments that are scheduled for the day. She selects the option to bulk message reminding each of the customers regarding their appointments and the time the customer should arrive at the clinic to attend to the appointment.  

Before, she reaches her office, all the reminders messages to the patient have been sent on their way. And of course, yesterday she had already called each of the customers reminding the customers regarding their visit and the documents the customer should bring along with them for the visit.

The customer service person is responsible for taking care of the patient appointment reminders, customer satisfaction surveys, patient engagement via social media and many other activities as required by the clinic.

The needs of the clinic will define the various campaigns that the clinic runs for their patients to develop an in-premise and off-premise customer relationship management processes. 

The customer service person is also responsible for maintaining a constant conversation with the patient to enable a superlative customer experience.
 

The Patient Activities

The customer Jennifer logs into her portal and schedules an appointment with Dr. Jane. She uploads her insurance details as part of her appointment details

Jennifer, attaches the latest reports, relevant for this doctor visit and the medications she is currently taking.

One day prior to the date of her appointment, jennifer gets a call from the clinic confirming her appointment and updates on any other documents she needs to carry with her)
   
The patient is the most important aspect of the entire healthcare workflow. Each of the activities and the processes within the hospital are moving towards a patient (customer) facing than being hospital or doctor facing.

The Patient starts her journey by anyone of these scenarios:

  • booking that first appointment with a doctor in a facility
  • booking for a health-checkup
  • booking for a radiology or pathology service, as referred to by a GP
  • and many others…   

 

Activity Interactions

Now that we have identified the various actors in a clinical setting it is important to understand the interactions between these actors. There is a need to consider the interaction between the actors to be connected to other actors and at other times the activities could be limited to a single actor.
 
However, the interactions between various actors also define another dimension to the entire workflow. For instance, the doctor – nurse interaction could have the nurse as the focal point and at other times the doctor as a focal point of interaction. Hence the activities that will be delivered to the user based on her role will depend on the interaction context.

Using the activity interaction map, the system will be able to setup the base set of activities and then progress from there to learn about any new interactions and activities to be performed. 

Newer activities are coded into the system by defining newer objects and the front end definitions will result in the generated page engine to present to the user the screen in which they need to enter the information. This will be achieved by using a combination of an object creator, rules engine and front end designer.

Additional Considerations

To develop a solution of this nature we present a list of features that should be included into the framework of the solution
 
Generated Pages: Using cognitive computing, the system will be able to present the doctor a Patients Health Record as a generated Generated Page which displays the details of the patient record on the basis of the current visit and diagnosis. Included in this generated page are actionable intelligence inputs presented to the doctor by the underlying congnitive computing enabled analytics platform.

Activities presented to the care provider on the basis of the Appointments, ward rounds, patient interactions via virtual visits – doctor is presented with the patient information when the visit is started.

The doctor should be able to type in regular statements and the system responds by pulling together relevant information regarding the patient. For instance, the doctor queries, “display the list of active medications the patient is on and display the list of lab tests that have been ordered in the past three months”. 


Speech Recognition and NLP: The Zen clinicals will heavily employ the speech recognition and NLP capabilities to allow the doctor to perform the following activities. It is important to get the doctor to move away from the system and focus on the patient treatment

  • The doctor will be able to dictate the details of the visit as a recording. While dictating, the system will alert the doctor regarding any mandatory information that is required for the visit, that had been missed out in the dictation. 
  • Secondly, the system will allow the doctor to see the mandatory form that needs to be filled for the patient visit or his ward round and indicate by voice commands the values that need to be selecteddeselected or chosen from a list of values etc  

 
Digital Assistant: The Zen Clinicals solution will have a digital assistant available for the doctor to help in performing any of the activities. These activities could be a resulting microinteraction or they could be statements submitted by the doctor to the system. Activities can be performed
 
Data Analytics: At the time of prescribing the medication for the patient, the doctor is presented with the list of medications sorted by their administration to other patients under similar parameters, by cost and availability at the patient location. The Zen Clinicals solution will be developed with a analytical data structure at its core.


Microinteractions: The system should have the ability to allow the users to define microinteractions as a set of rules and trigger criteria to generate activities or alerts based on the rules and trigger criteria. 

Alerts Engine: At the core of the Zen Clinicals framework is also an alerts engine that takes keeps track of all the results generated from any microinteraction and has the visibility of delivering these alerts via multiple channels (desktop alerts, mobile notifications or wearable notifications). The alert engine uses the presence definitions from the unified communications framework component to deliver the alerts to the user

Workflow Engine: The Zen Clinicals framework incorporates a workflow engine at its core to define the various workflow activities, such as authorizations, digital signatures and sign-offs for authorization activities, co-sign actions for activities, peer review and sign-off, order process workflows and many other such definitions.
 
Unified Communications Platform: The Zen Clinicals solution has a unified communication platform integrated into the core of the framework to enable the presence identification and communications framework from within the application. The users will be able to make use of multiple communication channels using this capability to share information seamlessly with their peers.
 
Face Recognition: 50 Face Recognition APIs – Data Science Central http://ow.ly/VNvhI