Month: February 2018

Artificial Intelligence #AI – the new hope for Pharma R&D – By Manishree Bhattacharya @ManishreeBhatt1


Pretty much every article starts with the challenges that pharmaceutical industry across the globe is facing. It is a difficult industry and everybody acknowledges that, considering the time to develop an original drug (10-15 years), the costs involved (last time I checked it was USD 2-3 billion), the high attrition rates of drug candidates (1 out of 5,000 or 10,000 leads make way for FDA approval), the tough regulatory environment which is varied across countries and geographies, and the rising pressures on pricing (pricing advantage for truly outcome-driven therapeutics). All of these, with the looming patent expiry, the imminent entry of generics, and the tantalizing RoIs, make it even more difficult.





Pic Credit: CreativeCommons.org

Well, technology is here to rescue. It will be a grave injustice to talk about technology implementation in pharma R&D in just one article, and because AI and ML are the current buzzwords, I thought, maybe, we could specifically discuss the role Artificial Intelligence plays in streamlining and improving success rates of pharma R&D. Before we go any further, let us have a quick look at the drug discovery process and the typical timelines associated.

More often than not, it takes more than a decade (sometimes > 15 years) for a new drug to enter the market. One should not forget if technologies and in-silico modeling are not effectively used in early stages of drug discovery, drug failure at a later stage is a significant waste of time and money. Now, pharma companies have already been using computational tools to conduct ADMET predictions and in-silico modeling, so what new has Artificial Intelligence to offer?
To answer this question, important will be to look at some of the AI initiatives and activities of key pharmaceutical companies. You may find initiatives where pharma companies are scouting for AI innovation via their open innovation program, so if you are a tech start-up, think about it.

The list is not exhaustive and is only for the purpose of illustration, but one can clearly see that leading pharma companies have already dipped their toes in Artificial Intelligence-mediated drug discovery and development.
Going back to the drug discovery process diagram, let us superimpose the AI-applications across the process.


Artificial Intelligence and Machine Learning algorithms, not only simplify the existing tasks/processes, it saves time, while adding significant value. Let us understand this better.
BioXcel Corporation, a biopharmaceutical company is working on integrating big data and AI into drug discovery process. One such product is EvolverAI which uses AI algorithms for drug discovery to find the best therapy, thus reducing drug failure. Evolver AI uses big data to screen through huge volumes of structured and unstructured data related to genes, proteins, disease pathways, targets, symptoms etc. within the field of Neuroscience. This is followed by creation of meta-data, which contains network-maps, linking pathophysiology of diseases with drugs. These meta-data are then fed into a decision matrix, which compares all the drugs. Using AI algorithms and human intelligence, several hypotheses are built based on the known linkages, of which the best hypothesis gets selected for future experiments and clinical trials, significantly reducing both time and risk of drug failures.

Exscientia, which has partnerships with both GSK and Sanofi, uses AI to learn best practices from drug discovery data, and helps researchers generate drug candidates in much lesser time.
The beauty of such algorithms is in their ability to go though various data-sets – from medical records, publications, clinical trial data, available data on disease pathways and drug-disease correlation to derive meaningful analysis that can help researchers in decision making. Similarly, screening through EHR, current and past clinical trials, and available publications on epidemiology, can help in site selection for clinical trials. Applications are many, and this is still an evolving area, with many developments happening in the stealth mode.
Many diseases such as cancers and neurodegenerative disorders, having high mortality and morbidity, have been debilitating for people across the globe, with pharma companies pouring in significant amount of money, yet with not-so-significant success rates.
Take Alzheimer’s for example, after three large clinical trials for solanezumab, which was called the breakthrough drug candidate, it hasn’t been able to display significant change in patient’s conditions as compared to a placebo. Solanezumab is an anti-Aβ mAbs, that targets Amyloid beta, which is one of the main components of the amyloid plaques found in the brains of Alzheimer’s patients. Does that mean researchers are looking in the wrong place? Is there a need to rethink the disease pathway and establish newer biomarkers to be targeted? THESE are the areas where AI and ML can help.
Similarly, increasingly the drug research community is realizing that not all drugs work on all patients. AI algorithms can help stratify patient population to identify what causes drug efficacy in some patients while nothing really in others – could be due to an aberration, mutation, any specific biomarker – and AI can help recognize the same in the most efficient manner possible.
And that is not all, there is a lot more – we are just scratching the surface, but one cannot deny that these technologies are the next hope for pharmaceutical companies. What do you think?
Source: Nature, Wall Street Journal, MIT Technology Review, Forbes, PR NewsWire, Micar21, SlideGeeks, BenchSci, Xconomy, MedCityNews, Company Websites

The article was first published on Ms. Manishree Bhattacharya’s LinkedIn Pulse Blog, here. Its been republished here with the authors’ permission.

Author

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Manishree Bhattacharya

Independent consulting – strategic research, industry analysis, healthtech evangelist, digital thought leadership (ex-NASSCOM, ex-Evalueserve)
Has over 8 years of experience in strategic research across healthcare, life sciences, software products, and start-ups, and has extensively worked with Indian and Global clients, helping in market analysis, digital evangelizing, start-up collaboration, competitive intelligence, and decision making

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KPIs on fingertips – Healthcare by Jyoti Sahai @JyotiSahai


During a recent conversation with the CEO-Doctor of a multi-specialty hospital our discussion veered towards how data-driven decision-making using analytic insights could benefit the hospital. His response, typical of most of the CEOs (for that matter from any industry) was – Oh! I really don’t need any analytics! All the facts I need to run my organization are on my finger-tips!



My takeaway from that conversation were the two keywords ‘facts‘ and ‘fingertips‘! For running a successful organization, you do always need to have near real-time relevant and critical (may be up to ten, one for each fingertip!) facts on what is happening within the company. However, just the facts (measures) may not always be sufficient to arrive at a decision unless those are benchmarked against the desired performance and/or trends over different periods for those measures. Deployment of analytics enables the stakeholders to have that additional edge over the decision-making, by making the exercise based more on validated data than just a gut feeling.

That set me thinking on what could be those top key performance indicators (KPIs) which if available on fingertips (at the click of a button) could aid a CEO in achieving the organizational objectives more effectively, and what could be the ones relevant for a hospital CEO!   .
I presume that any hospital CEO’s top priority is to strive to earn the patients’ trust, and that is possible only if the hospital could meet and exceed patient expectations.

Meeting the patient expectations

What a patient expects from the hospital is a treatment that is effective, timely and fair. The following KPIs keep the hospital CEO and other stakeholders informed on how effectively that is happening?

Treating the patients effectively …

The top hospital stakeholders should be worried if higher % of patients who have been already discharged (whether out-patients from day-care or inpatients with hospital-care) return to hospital for re-treatment or re-admittance for the same ailment. That will show that either the initial diagnosis was flawed, or some critical elements were missed out while administering the treatment. Either way it would be matter of great concern for the hospital CEO, who should always be aware of the Re-admittance Index – % of discharged patients who required re-treatment or re-admittance.

... and timely …

One of the most critical performance indicator within a day-care hospital is the TAT, the turnaround time – the elapsed time between entry of the patient in the hospital (registration) and start of consultation of that patient by the physician. Other important TATs that are tracked within a hospital include – for a test being conducted, the elapsed time between the ordering of the test till the report collection, and most importantly for an inpatient, the elapsed time between the decision to discharge and the actual vacating of the bed. Inordinate delays in these lead to irritated patients, increased costs, and avoidable queueing issues too. Typically hospitals set internal benchmarks, or compare with any available industry benchmarks, to track the various TATs. In case of inordinate delays, hospitals could carry out a root cause analysis and take preventive and corrective actions.
What any hospital CEO should strive for is that the TAT Index for any given period is less than 5%, that means not more than 5% patient-visits experience a delay beyond a set benchmark in treatment or in discharge.

… and fairly …

I remember once a CEO of a hospital was concerned about if any of the eleven consultants in the hospital were at any time prescribing investigations and/or medicines that were not warranted for the observed symptoms and the medical condition of the patient. Periodic audit of all prescriptions comparing those prescriptions with a defined set of rules (lines of treatment) for corresponding symptoms will give a fair idea of the deviations if any. What a CEO has to do to control it, is to always ensure that the Unfair Treatment Index (% of possible deviations from an appropriate line of treatment) is kept below the minimum acceptable tolerance benchmark.

… and thus earning patients’ trust!

A hospital may expect that it has earned a patient’s trust by providing treatment that is effective, timely and fair, but it can really know that for sure by arriving at the Patient Satisfaction (P-SAT) Index only. P-SAT can be derived by analyzing the feedbacks received from the patients, results of internal surveys, and the comments (adverse or commending) on the social media. A prudent CEO always depends upon the P-SAT Index to accurately gauge the extent of the hospital’s success and reputation.
We have now understood that patients’ trust can be earned by providing effective, timely and fair treatment. However none of that is possible unless the hospital itself is run efficiently and profitably.
How does the CEO keep track whether the hospital is run efficiently?

Managing the hospital operations efficiently

For meeting and exceeding the patients’ expectations it is imperative that the hospital operations including administrative and clinical processes are efficient and stable. Primarily it means that the all the hospital resources are used optimally, and are available for use when needed. The above-mentioned TAT Index is one such KPI. The following other KPIs too provide an indication of a hospital’s operational efficiency.

Are the resources and infrastructure used optimally?

Hospital resources and infrastructure, if not used optimally, lead to lost opportunity, frittering away of resources, and most importantly increase in operating costs. The Management has to ensure that the various Wards, Operation Theaters, Labs, and various equipments, and even the service providers (human resources) are available for providing service to the patient when needed. Out of these various parameters, tracking of the bed utilization (% of hospital beds occupied at any given time) is considered very critical for any large hospital as it has a direct impact on the efficiency of that hospital. A consistently low bed utilization could mean among other things, either faulty planning (resulting in over-investment) or a low P-SAT. On the other hand a consistently high bed utilization could lead to severe strain on resources and maybe result in declining quality of service.
Thus it is imperative that the hospital CEO constantly monitor the Bed Utilization Index.

Are the patients kept in hospital for a period that is necessary and sufficient?

One of the most critical KPIs for a hospital is the Average Length of Stay (ALOS) of inpatients for specific types of ailments or procedures carried out. The hospital could compare its such averages with either the industry benchmarks, or internally set benchmarks. For example assume that for a specific operation procedure (including the pre-operation and post-operation in-hopsital care) the ALOS is 6 days. If elsewhere in the industry the ALOS for the same procedure is 7 days, that will mean either your administrative and/or clinical processes are more efficient than others or you may be missing out on some necessary hospital-care (a point not in your favor). On the other hand if the ALOS elsewhere is 5 days, that will mean either you are providing some additional necessary services that others are not offering (a point in your favor) or your treatment more often is less efficient (your processes take extra time and/or resources for the same procedure).
Either way the CEO should keep a close watch on ALOS to optimize the services provided under the various procedures offered by the hospital.
However, even an efficiently run hospital having earned it patients’ trust to may fail if it is financially weak.

Monitoring the financial health of the hospital

For a hospital to ensure efficiency in its operations, it is imperative that its finances are stable and profitable. Without that the hospital will not be able to sustain its efficient operations for a longer period. It is the hospital CEO’s prime responsibility to ensure that that does not happen. The hospital CEO can depend upon the following KPIs to keep a check on the financial health of the hospital itself.

What is the hospital’s margin on an average for each patient-visit?

Whether you are an individual or an establishment, the universal fact remains that you cannot consistently spend more than what you earn if you have to sustain financially in the long-term.
What is critical for the hospital Management is to know what is the hospital earning on an average for each visit that a patient makes to it for treatment. Once ARPV is known for a period, and is compared with the average cost of operations for that period (ACPV), the hospital CEO knows whether the hospital operations at the current levels are sustainable or not.
Trends of ARPV and ACPV over a period give sufficient insights to the CEO to arrive at fair pricing of services, and take steps to manage optimal utilization of resources.
However a strong ARPV or a manageable ACPV alone will not be sufficient for financial stability unless the cash management is also strong.

Are the insurance claims being settled in time by the insurance companies?

Once a CEO of a 100-bed hospital was complaining that though he knew that the hospital had been having a strong revenue stream during that period, he was finding it difficult to pay on time for even the relatively small purchases made for materials and services. Why was that? A quick look at the hospital accounts revealed that (as is typical of all medium-large hospitals) almost 75% of the hospital revenue was derived thru insured patients, provided care under cash-less treatment schemes. It was also found that a substantial portion of that money was blocked in over-due claims submitted to the insurance companies and remaining outstanding for various reasons. That meant that the cash-flow was heavily dependent upon the timely settlement of insurance claims.
Any prudent CEO keeps a tight watch on the number of days claim outstanding (DCO) with the insurance companies; monitoring closely the TPAs – Third-party Administrators – ensuring that the claims are settled by the insurance companies as per agreed contractual terms. Timely settlement of insurance claims results in improved and predictable cash-flows and strengthens financial stability.
A hospital CEO may track the above-mentioned KPIs and ensure that the hospital is earning patients’ trust, and is operationally efficient and is financially stable too. But the litmus test of a hospital’s reputation and success is when its performance is compared with its peers, the other similar hospitals in the geography or with the same specialization.

Where does the hospital stand when compared with its peers?

Several independent agencies periodically rank the participating hospitals based on various performance factors, and the ranking could be geography-wise, type of hospital-wise, or specialty-wise.
For a CEO it is imperative that whichever ranking is most important for the hospital is thoroughly analyzed, and a proper strategy to improve/maintain the ranking in future put in place.

In conclusion

How does the CEO keep track of the above-mentioned top KPIs? The CEO’s Dashboardcould display the current status of the KPIs, available at any time at the click of a button (literally putting those on fingertips). A typical dashboard containing the critical KPIs could look like as shown below:
(The numbers and the traffic-light shown against each KPI in the dashboard are for illustration purpose only and do not represent any industry benchmark or desired value)
The above list contains the typical KPIs critical for gauging any hospital’s performance on various operational and financial parameters. However depending upon the criticality for a particular hospital, different and more relevant KPIs could replace those less relevant for that hospital.
By design, I have not included any KPIs or insights produced by clinical analytics, as those will be specialized and specific to each individual hospital.
My suggestion is that let the CEOs use their fingertips for recalling critical tricks of their trade and expertise only, and let an analytics system recall the KPIs for them whenever needed for reference!
[Glossary:
ACPV – Average Cost per Patient-Visit; ALOS – Average Length of Stay; ARPV – Average Revenue per Patient-Visit; DCO – Days Claims Outstanding; KPI – Key Performance Indicator; P-SAT – Patient Satisfaction; TAT – Turn-around Time]

Note: A version of this article also appears in my blog gyaan-alytics and more…

Author
Jyoti Sahai

Jyoti Sahai has over 42 years of experience in banking and IT industry, and is currently the CMD of Kavaii Business Analytics India. Kavaii provides analytic solutions in Healthcare and IT Services domains.

#DigitalHealth as a tool to Protect the National Health Protection Scheme by Dr. Oommen John @oommen_john


Author: Dr. Oommen John, Date: 12/02/2018

Digital Health would have a pivotal role towards efficient implementation of the National Health Protection Scheme announced in the #budget2018.


Healthcare related costs is one of the leading cause of impoverishment in India. In recent times, there has been a growing “trust deficit” between the consumers of healthcare services and the care providers.


The Budget 2018 announcement of ” #Ayushman Bharat ” aimed at financial risk protection from catastrophic healthcare expenses is a clearly articulated strategy towards providing Universal Health Coverage and India’s march towards achieving the UN sustainable development goals #SDGs.
Government sponsored health insurance schemes in India have run into the risk of becoming scams in the past, where the availability of insurance cover have been an incentive to perform investigations and procedures that were perhaps clinically unnecessary and in some cases physiologically impossible, such as males having their uterus removed ( procedure called hysterectomy, when one thinks of a male undergoing the same would roll hysterically !) and worse still these procedures being reimbursed by the insurance providers under the government sponsored schemes. 
There is an urgent need to empower the citizens to make informed choices and participate in shared decision making process. The National Health Portal has a wealth of information around health conditions and tools that aim to empower the citizens towards informed choices around health, there has also been concerted effort to make these available in regional languages.
Also, since a significant provision of secondary and tertiary care is in availed in the private sector, seamless referral mechanisms between the primary healthcare systems (which are mostly in the public sector and closer to where majority of the rural communities live) and the specialized private healthcare establishments would be fundamental to the successful implementation of the National Health Protection Scheme #NHPS.
The frontline healthcare workers empowered with #electronic health records of the populations they serve and using #clinical decision support tools could serve as the gatekeepers to triage and refer those needing higher level services into the healthcare delivery institutions. #electronic tracking of the referral would not only ensure that the healthcare delivery systems are not overwhelmed with sudden influx of a large number of patients wanting specialized services that the current healthsystems are ill-equipped to deliver but also serve as a regulatory mechanism for these well intended schemes from being misused and protect the vulnerable citizens from being exploited and their organs being sacrificed at the alters of greed (akin to the killing of the golden goose). Any well intended scheme is a potential scam unless robust mechanisms prevent them from being misused.
Health Systems generated Electronic health records or better still patient held electronic health records such as MyHealthRecord as envisaged by the ministry of health and family welfare along with functional regional and central health information exchanges would be the backbone for the national health protection scheme to be efficiently operationalized.
Currently, most insurance linked health care provision is administrated through third party agencies, while few of the government insurance schemes are cashless, Ayushman Bharat is an immediate opportunity to scale up #digitalhealth based real-time health insurance handshakes that enable citizens to avail the benefits of this scheme without being pulverized in bureaucratic pain in addition to their physical pain from undue delays for “preauthorization” before they can avail essential healthcare services.
The implementation plan of the #NationalHealthProtectionScheme is an opportunity to leverage #designthinking concepts and establish thought leadership towards integrated people centered healthcare systems
While we have several islands of excellence in #mhealth, many of them still at national level pilot stag , a national Digital Health Platform would also help connect these islands and help navigate through the muddy waters towards a well-functioning digital health ecosystem with an aim to ensure a level playing field for all the stakeholders in the healthcare delivery space, thereby paving the path for more efficient and transparent healthcare delivery.
More over a national digital health platform / grid backed by a robust health information exchange would also create an enabling environment for “start up entrepreneurs” to plug in and contribute to the transformative vision articulated by the government towards achieving universal health coverage for all Indians.

The article was first published in Dr. Oommen John’s LinkedIn Pulse page, its been re-published here with the authors’ permission

Author
Dr. Oommen John

is a Consultant Physician, Public Health Research and Policy Expert. He is the current President of the Indian Association for Medical Informatics and a Senior Research Fellow at the George Institute for Global Health

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#DIGITALHEALTH AS A TOOL TO PROTECT THE NATIONAL HEALTH PROTECTION SCHEME BY DR. OOMMEN JOHN @OOMMEN_JOHN


Glossary of Terms & Acronyms for Artificial Intelligence and Machine Learning

AI & Machine Learning Terms

Artificial intelligence The development of computers capable of tasks that typically require human intelligence. A machine’s ability to make decisions and perform tasks that simulate human intelligence and behavior.

Machine learning Using example data or experience to refine how computers make predictions or perform a task. A facet of AI that focuses on algorithms, allowing machines to learn without being programmed and change when exposed to new data.   

Deep learning A machine learning technique in which data is filtered through self-adjusting networks of math loosely inspired by neurons in the brain. The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.  

Supervised learning Showing software labeled example data, such as photographs, to teach a computer what to do. A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student; more common than unsupervised learning.  

Unsupervised learning Learning without annotated examples, just from experience of data or the world—trivial for humans but not generally practical for machines. Yet. A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis.  

Reinforcement learning Software that experiments with different actions to figure out how to maximize a virtual reward, such as scoring points in a game.  

Artificial general intelligence   As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them.

Large-scale Machine Learning Design of learning algorithms, as well as scaling existing algorithms, to work with extremely large data sets.

Deep Learning Model composed of inputs such as image or audio and several hidden layers of sub-models that serve as input for the next layer and ultimately an output or activation function.

Natural Language Processing (NLP) Algorithms that process human language input and convert it into understandable representations. The ability for a program to recognize human communication as it is meant to be understood. 

Collaborative Systems Models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans.

Computer Vision (Image Analytics) The process of pulling relevant information from an image or sets of images for advanced classification and analysis.

Algorithmic Game Theory and Computational Social Choice Systems that address the economic and social computing dimensions of AI, such as how systems can handle potentially misaligned incentives, including self-interested human participants or firms and the automated AI-based agents representing them.

Soft Robotics (Robotic Process Automation – RPA) Automation of repetitive tasks and common processes such as IT, customer servicing and sales without the need to transform existing IT system maps.

Algorithms: A set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own; classification, clustering, recommendation, and regression are four of the most popular types.

Artificial neural network (ANN): A learning model created to act like a human brain that solves tasks that are too difficult for traditional computer systems to solve.

Autonomic computing: A system’s capacity for adaptive self-management of its own resources for high-level computing functions without user input.

Chatbots: A chat robot (chatbot for short) that is designed to simulate a conversation with human users by communicating through text chats, voice commands, or both. They are a commonly used interface for computer programs that include AI capabilities.

Classification: Classification algorithms let machines assign a category to a data point based on training data.

Cluster analysis: A type of unsupervised learning used for exploratory data analysis to find hidden patterns or grouping in data; clusters are modeled with a measure of similarity defined by metrics such as Euclidean or probabilistic distance.

Clustering: Clustering algorithms let machines group data points or items into groups with similar characteristics.

Cognitive computing: A computerized model that mimics the way the human brain thinks. It involves self-learning through the use of data mining, natural language processing, and pattern recognition.

Convolutional neural network (CNN): A type of neural networks that identifies and makes sense of images.

Data mining: The examination of data sets to discover and mine patterns from that data that can be of further use.

Data science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into phenomenon via either structured or unstructured data.

Decision tree: A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart.

Fluent: A type of condition that can change over time.

Game AI: A form of AI specific to gaming that uses an algorithm to replace randomness. It is a computational behavior used in non-player characters to generate human-like intelligence and reaction-based actions taken by the player.

Genetic algorithm: An evolutionary algorithm based on principles of genetics and natural selection that is used to find optimal or near-optimal solutions to difficult problems that would otherwise take decades to solve.

Heuristic search techniques: Support that narrows down the search for optimal solutions for a problem by eliminating options that are incorrect.

Knowledge engineering: Focuses on building knowledge-based systems, including all of the scientific, technical, and social aspects of it.

Logic programming: A type of programming paradigm in which computation is carried out based on the knowledge repository of facts and rules; LISP and Prolog are two logic programming languages used for AI programming.

Machine intelligence: An umbrella term that encompasses machine learning, deep learning, and classical learning algorithms.

Machine perception: The ability for a system to receive and interpret data from the outside world similarly to how humans use our senses. This is typically done with attached hardware, though software is also usable.

Recurrent neural network (RNN): A type of neural network that makes sense of sequential information and recognizes patterns, and creates outputs based on those calculations.

Swarm behavior: From the perspective of the mathematical modeler, it is an emergent behavior arising from simple rules that are followed by individuals and does not involve any central coordination.

TerminologyDefinition
Automated communicationsAlso known as an interactive agent, or artificial conversational entity, these are computer programs which conduct a conversation via auditory or textual methods. For example, chatbots, mailbots.
Automated data analystAI solutions aimed at performing the job of data analysts and data scientists and bridging the gap between such roles and business imperatives. For example, these might include programs that are able to develop a deep understanding of customer preferences from data, identify high-risk customer groups and tailor interaction touch points in a manner personalised to such customers.
Automated operational and efficiency analystAI solutions targeted at increasing operational efficiency and reducing costs. These include AI programs and bots aimed at automating repetitive manual tasks such as identifying and correcting data and formatting mistakes, performing back office tasks and automating repetitive interactions with customers.
Automated research and information aggregationApplications of AI that involve aggregating and processing large volumes of information on a topic so as to generate meaningful insights. For example, aggregating information from research papers or medical journals for diagnosis support, identifying online hoax, bad reporting and statistics, and identifying plagiarised publications.
Automated sales analystAI-powered digital analysts for sales and marketing decisions. These programs are able to test a range of scenarios using internal and external data to predict the impact of marketing strategies such as promotions and campaigns, simulate ‘what if’ scenarios against multiple hypotheses and perform root cause analyses against business results.
Business decision makers/influencersA sub-set of participants in the survey who have identified themselves to be either in a decision making role or an influencing role in their current organisations. Some of the survey questions had been specifically targeted towards this group.
Decision support systemsDecision support systems (DSS) are a specific class of computerised information systems that support business and organisational decision-making activities.
Machine learningMachine learning is concerned with computer programs that automatically improve their performance through experience.
Predictive analyticsPredictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behaviour patterns–for example, sales forecasts, predicting customer churn and industrial
machine failure.
RoboticsRobotics deals with the design, construction, operation and use of robots, as well as computer systems for their control, sensory feedback and information processing. Environmental information such as imagery and sound are captured using a group of sensors and the same are processed using various computerised techniques for the robot to respond.
Virtual personal assistantsVirtual assistants use natural language processing (NLP) to match user text or voice input to executable commands. Many continually learn using AI techniques, including machine learning. For example, Apple’s Siri, Amazon’s Alexa, Google Now.
AI advisorsAI advisors are machines or systems that monitor employees’ progress and performance. They are responsible for the growth of the employee in the organisation and for the delivery of projects.
AI assistantsAI assistants are machines or systems or application programming interfaces ([APIs] a set of subroutine definitions, protocols and tools for building application software) that perform non-value adding services such as scheduling and email management.

Source: https://www.pwc.in/assets/pdfs/consulting/technology/data-and-analytics/artificial-intelligence-in-india-hype-or-reality/artificial-intelligence-in-india-hype-or-reality.pdf

References


[1]: pwc AI: https://www.pwc.com/ai

[2]: AI: The Complete Guide: 
https://www.wired.com/story/guide-artificial-intelligence/
[3]: https://dzone.com/articles/ai-glossary

Glossary of Healthcare & HealthIT Terms and Acronyms



ACO (Accountable Care Organization) MEDICARE’s outcomes-based contracting approach

Activity Diagram A UML Diagram that shows a workflow process, particularly focused on communication and the actors involved in that communication. Introduced as part of the HDF as part of the requirements analysis for HL7 standrads.

ADT – Admissions, Discharge & Transfer

ANSI American National Standards Institute. Founded in 1918, ANSI itself does not develop standards. ANSI’s roles include serving as the coordinator for U.S. voluntary standards efforts, acting as the approval body to recognize documents developed by other national organizations as American National Standards, acting as the U.S. representative in international and regional standards efforts, and serving as a clearinghouse for national and international standards development information.

Attribute Type The last part of an attribute name (suffix). Attribute type suffixes are rough classifiers for the meaning of the attribute. See also Data Type for contrast.

Authenticated Document A status in which a document or entry has been signed manually or electronically by one or more individuals who attest to its accuracy. No explicit determination is made that the assigned individual has performed the authentication. While the standard allows multiple instances of authentication, it would be typical to have a single instance of authentication, usually by the assigned individual.

Auxiliary Application An auxiliary application neither exerts control over, nor requests changes to a schedule. It is only concerned with gathering information about a particular schedule. It can be considered an “interested third- party,” in that it is interested in any changes to a particular schedule, but has no interest in changing it or controlling it in any way. It may gather information passively or actively. An auxiliary application passively collects information by receiving unsolicited updates from a filler application.

Arden Syntax an approach to specifying medical knowledge and clinical decision support rules in a form that is independent of any EHR and thus sharable across hospitals

ARRA (American Recovery and Reconstruction Act) the Obama administration’s 2009 economic stimulus bill

Blue Button an ASCII text based standard for heath information sharing first introduced by the Veteran’s Administration to facilitate access to records stored in VistA by their patients. The newer Blue Button + format provides both human and machine readable formats.

Bio-sensing wearables

A biosensor is an analytical device which converts a biological response into an electrical signal and wearables are on or in body accessories that enhance user experience. Biosensing wearables can monitor changes in physiology and the external environment. They are easy to use and provide useful, real-time information by allowing continuous physiological monitoring in a wide range of wearable forms.

CCD (Continuity of Care Document) an XML-based patient summary based on the CDA architecture

CCOW (Clinical Context Object Workshop) an HL7 standard for synchronizing and coordinating applications to automatically follow the patient, user (and other) contexts to allow the clinical user’s experience to resemble interacting with a single system, when they are using multiple, independent applications from many different systems

CCR (Continuity of Care Record) an XML-based patient summary format that preceded CDA

CCDA (Consolidated Clinical Document Architecture) the second revision of HL7’s CDA architecture that attempts to introduce more standard templates to facilitate information sharing (a mandate of Meaningful Use 2)

CDA (Clinical Document Architecture) an XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents

CDC (Centers for Disease Control and Prevention) the federal agency focused on disease in the community.

CA (Certificate Authority) an entity that digitally signs certificate requests and issues X.509 digital certificates that link a public key to attributes of its owner

CIMI (Clinical Information Modeling Initiative) an independent collaboration of major health providers improve the interoperability of healthcare information systems through shared and implementable clinical information models

CMS (Centers for Medicare & Medicaid Services) the component of the Department of Health and Human Services that administers the Medicare and Medicaid programs

CommonWell Alliance a group of major health IT companies that is working to achieve interoperability among their respective software products and services

Complete EHR an EHR software product that, by itself, is capable of meeting the requirements of certification and Meaningful Use

CONNECT ONC supported open source software for managing the centralized model of health information exchange

CPT (Current Procedural Terminology) the American Medical Association’s standard for coding medical procedures

De-identified Patient Health Information PHI from which all data elements that could allow the data to be traced back to the patient have been removed

DIRECT a set of ONC supported standards for secure exchange of health information using email

DNS (Domain Name System) the naming system for computers, services, or any resource connected to the Internet (or a private network). Among other things, it translates domain names (e.g. eBay.com) to the numerical IP addresses needed to locate Internet connected resources.

eHealth
The transfer of health resources and healthcare by electronic means, encompassing three main areas:
the delivery of health information, for health professionals and health consumers, through the Internet and telecommunications
 using the power of IT and e-commerce to improve public health services, e.g. through the education and training of health workers
 the use of e-commerce and e-business practices in health systems management

Electronic health records (EHR)
A set of records that clinicians control to co-ordinate their team work within and between healthcare teams.

Electronic patient health records (EPR)
A set of records that the patient controls and which allows the patient to work with their clinical team across institutional boundaries.

EDI/X12 a format for electronic messaging that utilizes cryptic but compact notation primarily to support computer-to-computer commercial information exchange

eHealth Exchange a set of standards, services and policies that enable secure nationwide, Internet-based health information exchange using CONNECT or one of the commercial HIE products that support eHealth Exchange

EHR (Electronic Health Record) a stakeholder wide electronic record of a patient’s complete health situation

EHR Certification a set of technical requirements developed by ONC that, if met, quality an EHR to be used by an Eligible Professional to achieve Meaningful Use

Eligible Professionals (Medicaid) health providers who are eligible for Medicaid Meaningful Use payments: doctors of medicine, osteopathy, dental surgery, dental medicine, nurse practitioners, nurse certified nurse-midwifes, and physician assistants who working in a Federally Qualified Health Center or Rural Health Clinic that is led by a physician assistant

Eligible Professionals (Medicare) health providers who are eligible for Medicare Meaningful Use payments: doctors of medicine, osteopathy, dental surgery, dental medicine, podiatry, optometry and chiropractic

EMPI an enterprise master patient index

EMR (Electronic Medical Record) an electronic record used by a licensed professional care provider

GELLO a programming language intended for use as a standard query and expression language for clinical decision support. Now compatible with the HL7 version 3.0 Reference Information Model (RIM).

HDF (HL7 Development Framework) the framework used by HL7 to produce specifications for data, messaging process and other standards

Health System a network of providers that are affiliated for the more integrated delivery of care

Healtheway an ONC supported public-private partnership to promote nationwide health information exchange via the eHealth Exchange

HIE (Health Information Exchange) the sharing of digital health information by the various stakeholders involved, including the patient

HIMSS (Healthcare Information and Management Systems Society) describes itself as a “a global, cause-based, not-for-profit organization focused on better health through information technology (IT)”

HIPAA (Health Insurance Portability and Accountability Act of 1996) legislation intended to secure health insurance for employees changing jobs and simplify administration with electronic transactions. It also defines the rules concerning patient privacy and security for PHI

HISP (Health ISP) a component of Direct that provides a provider directory, secure email addresses and public-key infrastructure (PKI)

HIT (Health Information Technology) the set of tools needed to facilitate electronic documentation and management of healthcare delivery

HITSP (Healthcare Information Technology Standards Panel) a public/private partnership to promote interoperability through standards

HL7 (Health Level 7) a not-for-profit global organization to establish standards for interoperability

HMO (Health Maintenance Organization) an organization that provides managed healthcare on a prepaid basis. Employers with 25 or more employees must offer federally certified HMO options if they offer traditional healthcare options

hQuery an ONC funded, open source effort to develop a generalized set of distributed queries across diverse EHRs for purposes such as clinical research

HTTP (Hypertext Transfer Protocol) a query-response protocol used to transfer information between web browsers and connected servers. HTTPS is the secure version.

ICD (International Classification of Disease) the World Health Organization’s almost universally used standard codes for diagnoses. The current version is ICD-10 and it was adopted in the US on October 1, 2015 — well after most other advanced countries had moved to it.

IHTSDO (International Health Terminology Standard Development Organisation) the multinational organization that maintains SNOMED

IHIP Integrated Health Information Platform. An Integrated Health Information Platform (IHIP) is being setup by the Ministry of Health and Family Welfare (MoHFW). The primary objective of IHIP is to enable the creation of standards compliant Electronic Health Records (EHRs) of the citizens on a pan-India basis along with the integration and interoperability of the EHRs through a comprehensive Health Information Exchange (HIE) as part of this centralized accessible platform.

IP Address a 32 bit (the standard is changing to 128 bit to accommodate Internet growth) number assigned to each device in an Internet Protocol network and that indicates where it is in that network.

I2b2 (Informatics for Integrating Biology and the Bedside) a scalable query framework for exploration of clinical and genomic data for research to design targeted therapies for individual patients with diseases having genetic origins

Interoperability the ability of diverse information systems to seamlessly share data and coordinate on tasks involving multiple systems.

LDAP (Lightweight Directory Access Protocol) is a protocol for accessing (including searching) and maintaining distributed directory information services (such as an email directory) over an IP network.

LOINC (Logical Observation Identifiers Names and Codes) the Regenstrief Institute’s standard for laboratory and clinical observations

Meaningful Use a set of usage requirements defined in three stages by ONC under which eligible professionals are paid for adopting a certified EHR

MedDRA (Medical Dictionary for Regulatory Activities) the International Conference on Harmonisation’s classification of adverse event information associated with the use of biopharmaceuticals and other medical products

Medicaid the joint federal/state program to provide healthcare services to poor and some disabled US citizens

Medicare the federally operated program to provide healthcare services to US citizens over the age of 65

MIME (Multipurpose Internet Mail Extensions) the Internet standard for the format of email attachments used in Direct. S/MIME is the secure version.

MLM (Medical Logic Module) the basic unit in the Arden Syntax that contains sufficient medical knowledge and rules to make one clinical decision.

Modular EHR a software component that delivers at least one of the key services required of a Certified EHR

Moodle (Modular Object-Oriented Dynamic Learning Environment) is one of the most popular open source Course Management Systems (CMS). It is written in PHP programming language and distributed under the GNU General Public License. Moodle was created by Martin Dougiama to help educators to create online courses with a focus on interaction and collaborative construction of content, and it is in continual evolution.

Mobile health (mHealth)
Medical and public health practice supported by mobile devices (mobile phones, smart phones and tablets), patient monitoring devices, personal digital assistants (PDAs), and other wireless devices. Utilising a mobile phone’s core voice and short messaging service (SMS) and more complex functionalities and applications including general packet radio service (GPRS), third and fourth generation mobile telecommunications (3G and 4G systems), global positioning system (GPS), and Bluetooth technology.

Mobile applications (apps)
A software application that can run on a mobile platform (i.e. a handheld commercial off-theshelf computing platform, with or without wireless connectivity) or a web-based software application that is tailored to a mobile platform but is executed on a server.

MPI (Master Patient Index) software to provide correct matching of patients across multiple software systems, typically within a health enterprise

MUMPS (Massachusetts General Utility Multi-Programming System) an integrated programming language and file management system designed in the late 1960’s for medical data processing that is the basis for some of the most widely installed enterprise health information systems

NDC (National Drug Codes) the Food and Drug Administration’s numbering system for all medications commercially available in the US

ONC (Office of the National Coordinator for Health Information Technology) the agency created in 2004 within the Department of Health and Human Services to promote the deployment of HIT in the US

Online patient communities

Online discussion groups allowing patients to learn from peers and professionals including how to understand their own data. They provide access to relevant, timely information and support others with similar conditions.

Outcomes-based Contract an approach to pay for healthcare that rewards physician performance against certain defined quality metrics when combined with a lower than predicted cost of care

Participatory medicine
Patients and clinicians work together to improve the patient’s health – in which patients have equal access to all data, are case managers of their own illness and co-producers of their own health. Primary care professionals become gateways not gatekeepers.

Patient portal
A website that gives patients access to the data and information in their electronic health record. Can also be used to book appointments and order repeat prescriptions.

Personal health records (PHRs)

A set of records that the patient controls and enables users to see who wrote what when and what for.

P4P (Pay for Performance) an approach to pay for healthcare that rewards physician performance against certain defined quality metrics

PCMH (Patient-Centered Medical Home) a team based healthcare delivery model often particularly focused on the management of chronic disease

PCP (Primary Care Physician) the generalist in a patient’s care team who assumes overall responsibility for all their health issues and often the gatekeeper who must generate referrals to specialists

PHI (Protected Health Information) any health or health related information that can be related back to a specific patient. PHI is subject to HIPAA regulations.

PKI (Public Key Infrastructure) a widely used system for protection of documents, messages and other data that rests on a pair of public and private keys to allow for a variety of use cases

Private Key the protected (known only to its owner) part of the special pair of numbers used to encrypt documents using PKI

Provider health professionals including physicians, nurse practitioners, physicians’ assistants that are engaged in direct patient care

Public Key the public part of the special pair of numbers used to encrypt documents using PKI

RA (Registration Authority) an entity that collects information for the purpose of verifying the identity of an individual or organization and produces a certificate request

Synthetic Health Data facsimile clinical data created by a software system to realistically resemble actual patient data

Templates (CDA) the reusable basic XML-based building blocks of a CDA document that can represent the entire document, its sections or the data entries within a section

Read Codes a hierarchical clinical terminology system used in General Practice in the United Kingdom

Resource Description Framework (RDF) a method for describing or modeling of information on the web using subject-predicate-object expressions (triples) in the form of subject-predicate-object expressions that could be used to represent health ontologies (SNOMED, ICD-1)

RIM (Reference Information Model) a pictorial representation of the HL7 clinical data (domains) that illustrates the life cycle of an HL7 message or groups of related messages

Semantic web the proposed next generation of web in which technologies like RDF would create a “web of data” in which browsers (and other tools) could “understand” the content of web pages

SMTP (Simplified Mail Transport Protocol) the Internet standard for email used by Direct. The secure version is S/SMTP

SNOMED (Standard Nomenclature of Medicine) a comprehensive, hierarchical healthcare terminology system.

SNOMED CT (Standard Nomenclature of Medicine) SNOMED subset for the electronic health record. SNOMED CT: Is the most comprehensive, multilingual clinical healthcare terminology in the world. Is a resource with comprehensive, scientifically validated clinical content. Enables consistent, processable representation of clinical content in electronic health records. Is mapped to other international standards.  Is already used in more than fifty countries

When implemented in software applications, SNOMED CT can be used to represent clinically relevant information consistently, reliably and comprehensively as an integral part of producing electronic health information.

Technology enabled Care (TEC)
The use of technology to enhance the quality and cost-effectiveness of care and support and improve outcomes for individuals through the application of technology (including, but not limited to, the use of telecare, telehealth, and mobile health and wellbeing) as an integral part of the care and support process.

Telecare
The continuous, automatic and remote monitoring of activity/lifestyle changes over time, providing real time alerts or calls for help in emergencies and helping to manage the risks associated with independent living, enabling people to live independently for longer, particularly those who require a combination of health and social care.

Telehealth and Telehealth
Telehealth involves the consistent and accurate remote monitoring and management of a health condition including vital signs monitoring. It involves the exchange of information between patient and HCPs to identify trends or changes in the patient’s condition, helping to avoid hospital admissions, support early discharge and improve self-care. Telehealth helps educate, train and support people to self-care.

Telemedicine

Telemedicine uses telecommunication and electronic information technologies to provide clinical healthcare at a distance, improving access to medical services and specialists. It permits communications between patient and medical staff as well the transmission of medical, imaging and health informatics data from one site to another. New forms of telemedicine include videotelephony, advanced diagnostics and telemedical devices to support home care.

ToC (Transition of Care Initiative) the effort to develop a standard electronic clinical summary for transitions of care from one venue to another

TPO HIPAA exception for providers, insurance companies and other health-care entities to exchange information necessary for Treatment, Payment or Operations of healthcare businesses

VDT (View, Download, Transmit) a requirement of Meaningful Use Stage 2 that patients view, download or transmit their health information

VistA (Veterans Health Information Systems and Technology Architecture) the Veteran’s Administration’s system wide, MUMPS based health information infrastructure

X.509 digital certificate the technical name for an electronic document issued by a CA that uses a digital signature to bind a public key with an identity based on information from an RA

XDR (External Data Representation) an operating system and transport method agnostic mechanism for exchanging data that is encoded/decoded into/from the XDR format.

XDM (IHE Cross Enterprise Document Media Interchange) a standard mechanism for including both documents and meta-data in zip format using agreed upon conventions for directory structure and location of files.

XDS (Cross-Enterprise Document Sharing) the use of federated document repositories and a document registry to create a longitudinal record of information about a patient

XML (Xtensible Markup Language) a widely used standard for machine and human readable electronic documents and the language used to define CDA templates

XMPI a cross organizational master patient index capable of dealing with many unaffiliated hospitals and health systems

LMIS – laboratory information management system

RIS – Radiology Information System

PACS – Picture Archival and Communications System 

DICOM – Digital Imaging and Communications in Medicine


References

[1]: Glossary of Terms on HL7: 
https://www.hl7.org/documentcenter/public/calendarofevents/FirstTime/Glossary%20of%20terms.pdf

[2]: HEALTH INFORMATICS ON FHIR: https://www.coursera.org/learn/fhir

[3]: pwc AI: https://www.pwc.com/ai

[4]: Connected health: How digital technology is transforming health and social care: 
https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/deloitte-uk-connected-health.pdf

Consent Fatigue by Karunakar Rayker @krayker


Recently, the Justice SriKrishna Committee came out with its draft White Paper on Data Protection framework for India. One of the key issues mentioned in the report was regarding Consent Fatigue. While the Whitepaper delves into the Consent issue at a policy level, we can see it at a micro level & around us in our everyday lives. Let us explore the issue through the eyes of an ordinary user, Ramesh.




1984 was a memorable year for Ramesh, the year he got his first computer, the ZX Spectrum. He was the smartest kid in the block & he loved his gadgets, the ZX Spectrum & Samurai Game Console. His favorite movie was The Terminator. Ramesh was proud of his technical prowess and way ahead of his time.

Fast forward a few years, he’s now a successful doctor. By the time he was done with higher education & internship, something drastically changed all around him. Years of burying himself in the books, left him with very little time to chase gadgets & technology. He now jokes that he’s a tech luddite & all at sea with the latest phones, apps and software.
Ramesh now finds himself as an average person in a digital first world, his window to the world is a digital interface. Services of various kinds from government, banking, medical, social media to entertainment are all digital; consumed in various devices – a TV, a smartphone, game console or a PC. He has to decide choosing/ subscribing/ paying for his devices & services to securing and keeping the software of his various devices up to date.
He laughs at his own naivety, when he’s assaulted with an array of vague to verbose dialog boxes, presented with choices he has difficulty in comprehending, some confusing, others being cryptic or even frightening. He talks to himself “Of course I can read up; but hey, not every person is a software engineer!”

He wonders at the barrage of decision making he faces, which only grows over time. Is he alone in giving up, in throwing his hands up in despair? Is he at fault, OR couldn’t it have been simpler? Ramesh isn’t alone in this quandary. We all are ‘users’ in this digital age, and our interface is broken. We get exasperated, some to a smaller extent, while others simply give up!


image credit: https://www.pexels.com/photo/design-desk-display-eyewear-313690/


Do they design the software intentionally to be tough? No. Were the App/ software developers inept? Probably not.Didn’t they think of the users? Maybe not.


For regular users like Ramesh, the transition to a digital world has not been seamless one. Mistakes may have been made by the Service/ Software developers, and as a result, we all face the challenges while paying our bills, installing an app or even hopping onto a Metro.


The average user just wants an interface, something he or she can understand & one that talks to him better! Could all of this confusion and exasperation be avoided?

The Answer is yes, to a great extent, right while developing the interface of the product/ service. By user-testing & removing bulk of the flaws early in the development cycle, the Product/ Service can be then be refined and bettered continuously. In fact we have been seeing it over the years. Every following hardware or software iteration has been improved upon, and one, the user readily accepted (even if it did not address the problem completely, but was not as annoying as the previous one).


Ramesh complains that he has trouble on his return to normal computing – that is using a PC, a Smartphone, WiFi, Smart TV etc. He’s annoyed with all those popup messages, Anti-Virus alerts, updates, acronyms & he simply clicks “yes” to ALL, saying it’s overwhelming!


Let’s try to understand his angst, break up his pain points.

Dialog Box Fatigue

This debate has been going on for the longest time in the way security has been handled in Unix/ Linux & Windows. At least until Microsoft handled it better in Windows 7. For reasons best known to Microsoft, in early versions of windows, Security wasn’t the top priority. This led to a lot of issues with virus & malware running riot. When Microsoft finally got around to addressing security issue in Windows Vista – it was a nightmare for the user. UAC or User Account Control was the silver bullet to all the years of criticism that Microsoft had to cop. But it was the User who finally had to cope with the constant bombardment of Dialog Boxes.

Want to install a Software? UAC.

Or gems like this:

“Are you really sure you want to quit.” after the “Are you sure you want to quit.”



via GIPHY


Worse still, in this case Dialog box fatigue led to crafty, devious and enterprising people to float ads which mimicked the OS and led to Malware or software asking elevated privileges/ root access. Another unintended nightmare to deal with. The temporary solution was to retrain users to do it the right way. Ugh. Things have NOT changed drastically though, the same ill plagues many a software. You cannot fault a user for their inability to understand all the messages a software throws.

Security Fatigue

There are Software engineers complain about “too much Security”. Most are overwhelmed by the alerts from banks to stay wary of Spoofing, Phishing, Smishing attacks. “Smishing” “Catfishing” “Doxing” have just been added to the dictionary!
The IT admin asks employees to stay away from clicking random emails because it may lead to a corporate hack. The technically savvy ask others not to install that shiny new Wallpaper app they’ve just seen – because you never know. There are a raft of new Password Apps to remember all passwords – but we still have trouble remembering that one password for that app! Most passwords are too simple, a combination of their identity (Name, Place, DoB, their Pet’s name etc) – guessable, or too weak (password, for example; or the most used and least safe password “123456 ” !

App Update Fatigue

An average user has to deal with his phone, laptop, PC, and all those devices at home. Many were spooked reading recent reports of users updating their latest TV, only to see it get bricked (Samsung/ UK). Another one said “Update your drone, or it will not fly” Thank you DJI!

Jargon Fatigue

Most school or college going kids or older smartphone users are no strangers to acronyms. A few years back kids typed away in T9 on their old Nokia without once looking at the phone! Others can converse without vowels – the SMS lingo. But, Catfishing, Smishing, rootkit, SSL, https, 2 factor authentication, IMAP, Doxing, seem like a stretch. Even for software professionals!

The average user out there wishes there were fewer jargon. After all, the promise of technology was to make our lives easier.

A Fatigued user

As a result of all these constant needless interactions, the average user flips, resulting in…
– Impulsive “Yes-to-All”
– Takes Random/ easiest decisions
– Resigned to Fate
– Stressed out, Feels out of control


via GIPHY

Which is to say the most disappointing outcome, the whole premise of the Product/ Service developer was to let an average user like Ramesh was to make an “informed” choice, and not ending in indecision. Excess choices and forcing the user to ponder at every turn leads to an overload & Decision fatigue!

Houston, We have a Consent Problem

By now, It’s amply clear that the user is subjected to a barrage of queries everyday, all day long. Decision making or at it’s heart “CONSENT” in most of his activities, most of which have a digital interface. The user should NOT have to exercise caution, tax his brain for every single action. Rather, the focus should be to involve the user in making ONLY critical decisions during any requirement for consent. Burdening a user leads to consent fatigue.

Why should I say yes to this?Which one should I say yes to?Why am I forced to choose? Can I skip it?Why aren’t more choices available?


Microsoft acted immediately in their next version – Windows 7, removed unnecessary dialog boxes, reduced it to a third. Managed QR code is used in many apps to ease the Consent. The choice is eclectic, there’s no Silver bullet for every situation, BUT, every succeeding effort should be one to reduce errors by users & not make it a chore, while enriching the experience and the interaction joyful.

Ramesh marvels at how much things have changed over the years, but he says there’s much to be fixed, before we can say we have truly arrived.

Where we stand today & the way forward

According to a study, an average person takes 70 conscious decisions per day! Add to it sundry everyday decisions like what to eat or wear, constant flow of Emails, Text messages & noise from Social Media. An average user is overwhelmed with choices & fatigue sets in quickly for even the most determined.

Awareness of this reality of user fatigue should be on top of the mind for Product/ Service providers to effectively address the same.

‘Copy’ or the way the app communicates to the user via the GUI (Graphical User Interface) or VUI (Voice User Interface) can help users greatly. Simple, Jargon-free, non-cryptic & clear words or instructions will help for starter. Often the user is lost or unable to understand the error message or what is being asked of him, for lack of contextual information or otherwise. Investing time to clean up the UX Copy can help mitigate such issues.

While Copy is just one aspect of it, reducing Cognitive Overload, or Information overload, or improving overall user experience may need multiple levels of fine tuning throughout the lifecycle of the Product or Service.

Optimal User Experience is now the challenge that differentiates the great from the “merely good”. For an average user, he/ she does not see or appreciate the technologies or changes behind the scenes. They only see and experience the product primarily via the interface. User Experience, IS the chief differentiator.


An enriched User Experience can help deliver more engaging, personalised and more meaningful interactions, one that will endear a service for a user to stick to a long time. Every platform/ service has its own set of challenges & its own unique solution. Focussing on the user’s needs closely helps in identifying the gaps, the areas of frustration & reduce potential issues.

Build a strong & passionate UX team, involve the entire team – from Engineering, Sales & Marketing, to Customer Support. After all, great products are a result of great work from a great team!

Further Reading

Author
Karunakar Rayker

Designer, User Experience Consultant, Technophile, Artist, Photographer. Karunakar, has more than 17 years of experience in designing and building solutions. He shares his knowledge and experience via his very active, resourceful and insightful twitter feed @krayker

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Consent Fatigue by Karunakar Rayker @krayker


Train your Mind to be an Entrepreneur By Priyanka Singh @1_priyankasingh


I have often read these powerful words, Entrepreneurship is for those who can think big. It was almost in contrast to my personality of being someone ambitious yet complacent with the success I would find with my sincere work. To think big, probably you have to be the person who constantly strives for success & works towards climbing the upper most pedestal even before you have climbed the nearest next. 

The Article was first published by Ms. Priyanka Singh on her linkedin pulse blog, the article is republished here with the authors’ permission


It seemed like a distant thought that I would one day venture out on the journey of Entrepreneurship, when I hardly believed in thinking beyond the goal in focus. It always was so unlike me to broaden my horizon and to have a vision. As Kevin D Johnson (Founder & CEO of Johnson Media Inc.) mentions in his book -The Entrepreneur Mind, Entrepreneur’s job is not to think out of the box but more to own the box. Sure, things did change over the last few years, with a little nudge from a motivating partner. The new calling was to ‘create’ something of our own, to practice what we have done for assets owned by big corporates in our professional stint so far, the only big limitation being resources of course.

So, what does it take really to be an entrepreneur, I figured out over the last two years, it is nothing but the ‘mindset’. If you do not possess one favoring entrepreneurship, yet dream to create something of your own, you must ‘cultivate’ one. Train your mind to be an entrepreneur. You must most certainly build the entrepreneurial mindset before you actually embark on your remarkable expedition. Here are a few of my observations towards training my mind:

Believe in your Plan: Nothing gives you a thrust more than passion for something. If you set out to chase something you are convinced about, it is a lot easier for you to pursue partners & your potential consumers. When you are still on the drawing board, there may be an ambiguity about the how but never be ambiguous about why of what you plan to do. Your why can be internally or externally motivated or you may have more than one, but it always helps to have it in place. It acts as a solid anchor for you to be reassured with your plan.

Don’t wait for the perfect timing: People have often associated entrepreneurship with taking risks, maybe that’s how it took me about 3 years to step aside everything else & get into the shoes of a founder. Most of us evaluate the risk and the opportunity cost based on our financial & professional achievement at that very time and delay taking that first step. Little do we realize, that as we procrastinate the decision, the stake is always more.

The First Step is never a full-fledged Establishment: I am sure if you have spoken at length with your friends or colleagues who are first generation business owners, you would know that their first step towards their venture was way before they actually established their company. The first step is to have an idea that when put to test with the target consumer will invoke a favorable response, as it can in some way contribute to their new & better ways of life. The inception of your business is in your mind & that’s your first step towards entrepreneurship.

Have Obligations & Deadlines: Stress is not always counter-productive, isn’t it? Having obligations and deadlines will lead to stress & it works beautifully to keep you true to your path especially if you start your business all alone. In case of a partnership, partners act as a harness and keep you on your toes and you do the same with them, because everything you or they do is potentially at the risk of objection by partners. However when you start solo, you have a tendency to be too comfortable with a pace of your own. Having obligations & deadlines carved out will ensure you perform better. Probably, which is why the larger corporations have shareholders, excluding the financial prudence, having them on board makes you liable for & answerable for your performance & that creates an atmosphere of positive stress. A small example is having invested my money on registering my company itself put me in an obligation to move forward. Another one is having tested my idea with a few consumer with a positive feedback, brought in another obligation to go for it rather repenting in later years and feel sorry to have let it pass.

Read, listen, talk & Get Inspired: The most crucial step towards training the mind is to read or listen to people who have made it Big & talk to them about your idea at any given opportunity. Inspiration to mind is like water to plants. The more inspired you keep your mind by emulating success stories, the better the chances that you will be proud of your own journey.

PS: My Training is ongoing, as I transcend a little every day, to dream big & inch closer to realize it. As I often joke around with my pals, I shall set an example, a good one or a bad one, the time shall disclose.

Author
Priyanka Singh

Branding Enthusiast | Entrepreneur in Making | Freelance Blogger & Writer. I have been working with healthcare in brand management for more than 7 years.Soon launching my food & nutrition brand in India with a vision to make people healthy and beautiful naturally
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