Top 8 Healthcare Predictions for 2019 by Reenita Das, @ReenitaDas - @FS_healthcare

What can you look forward to in healthcare in 2019? The debate expects to get hotter between AI vs. Physicians, Consumer vs. Clinical, Human empathy vs. Machine Intelligence as many new players enter the ecosystem

We have been writing the predictions for healthcare every year now for the past 10 years. We also review back how we did each year and each year we are getting to be more accurate. The 2018 predictions that were released in December 2017 were almost 98% accurate and each one of them panned out during the course of the year.

Globally, 2019 will be a year of value-based care as we expect the ‘outcomes-based care’ focus to globalize. This will trigger maturation of risk-sharing in solution contracting between providers and drug/device original equipment manufacturers (OEMs), driving business value for providers. Furthermore, access to affordable and quality care will be key political agendas for upcoming 2019 elections in emerging markets such as Asia, Africa and, Central and Eastern European countries. As the lines between retail, IT and healthcare industries continue to blur, during 2019, Google, Apple, Facebook and Amazon (GAFA) in the West, and Baidu, Ali Health, Tencent (BAT) in East will start to dominate the Individual Care space. Non-traditional digital marketplace providers such as Ali Health, Tencent, Amazon, Google, Apple, Microsoft, and IBM among other will dominate the home health space, providing the required impetus to public health systems to ensure accessibility and affordability of care. We also anticipate future drugs and devices R&D investments will be more targeted to meet the unique needs of emerging markets in Asia. Finally, we anticipate 2019 will be a reality check for two of the most hyped technologies for healthcare of this decade, viz. artificial intelligence (AI) and blockchain.

The top 8 predictions for global healthcare for 2019 are as follows:

Prediction #1: 15% of global healthcare spending will be tied to Value-based Models 
During 2019, the healthcare industry will continue to transit to the value-based model. We anticipate that by end of 2019, up to 15% of global healthcare spending will be tied in some form with Value/outcome based care concepts. The impetus for this shift will be more exigent for countries that currently spend nearly 10% or more of their GDP on healthcare spend [e.g., the United States, Netherlands, Sweden, France, Germany, Canada, and Japan among others]. During 2019, VBC initiative will continue to transition from economic model/cost-effectiveness measures to more health outcomes and treatment focus - by means of data-driven risk sharing frameworks and sustainable reimbursement model that benefits both providers and payers.

Prediction #2: Artificial Intelligence (AI) for healthcare IT Application will cross $1.7 billion by 2019
During 2019, AI across clinical and non-clinical use cases will show hard results further bolstering the growth of in healthcare space. We expect AI for Healthcare IT application market to cross $1.7 billion by end of 2019. We further anticipate that by operationalizing AI platforms across select healthcare workflows would result in 10–15% productivity gain over the next 2-3 year. However, the pricing for AI solutions remains critical as end-users are often not convinced to dedicate an additional budget for such IT capabilities. A cost effective approach with clear evidence for potential ROI for both parties can help sustain the market growth. Throughout 2019, AI and machine learning will further evolve human and machine interaction. More specifically, AI will begin to see fruition, particularly in the imaging diagnostic, drug discovery, and risk analytics applications.

Prediction #3: Digital health tech catering to out of hospital will grow by 30% and cross $25 billion.
During 2019, the application of digital health will continue to go far beyond the traditional system and empower individuals to be able to manage their own health. Based on our estimates, it is expected that digital health tech catering to out of hospital settings will grow by 30% to cross $25 billion market globally by end of 2019. Increasing cost burden from chronic health conditions and aging population will be the chief driver for digital health solution such as RPM devices, telehealth platforms, PERS, and mHealth applications. Furthermore, favorable reimbursement policies towards clinically relevant digital health applications will continue to expand care delivery models beyond physical medicine to include behavioral health, digital wellness therapies, dentistry, nutrition, and prescription management.

Prediction #4: Asia becomes the New Local Innovation Hub for Global Drug and Device OEMs
Historically, a majority of medical innovation pipeline has flowed from West to East. Now with emerging markets contributing 20-30% of the pharmaceutical industry’s value with a double-digit growth (10-15%), a string of global drug and device OEMs are attempting to upend that trend with new products tailored to Asian bodies, lifestyles, and purchasing parity (affordability). Entailing this we anticipate, by 2019, up to 10% of healthcare R&D will be invested to localize innovation for emerging markets in Asia. For instance, Asia-pacific is the strongest market in terms of growth, with more than 30% of the global late-stage trials for cell therapy alone. Moreover, we believe Asia-Pacific will witness the genomics revolution in the next few years and particularly, China will take a leading role in Asia’s genomics space. Entailing this changing paradigm of product development and geographic rollouts – we believe there will be a rise in “unicorn start-ups” (valued over $1 billion) and foreign direct investment riding on increasing demand for healthcare services, an aging population, and rising income levels.

Prediction #5: Analytics shifts from Big Data to Meaningful Small Data by Hospital Specialty
As the healthcare industry gets comfortable with data management workflows, we foresee a high number of specialty-specific analytics solutions will gain prominence among providers striving to investigate drug utilization, treatment variability, clinical trial eligibility, billing discrepancy, and self-care program attribution specific to major chronic conditions. We predict that by end of 2019, 50% of all healthcare companies will have resources dedicated to accessing, sharing, and analyzing real-world evidence for use across their organizations. Moving forward, the primary goals for healthcare payers and providers leveraging analytics capabilities will include; population health management (Identify at-risk individuals), identify and coerce to best treatment pathways (lowest cost, best outcomes), and operational automation by patients, payers, physicians, and procedures. Additionally, the convergence of AI and analytics capabilities will continue to advance augmented analytics capabilities to mainstream adoption in the next 2-3 years’ timeframe.

Prediction #6: Healthcare will be a dominant vertical in voice applications
Healthcare is at a tipping point with voice - specialized players such as Nuance, Orbita and leading tech companies (Amazon, Apple, Google, and Microsoft) are catching on with targeted voice technologies suited for healthcare industry use cases. We anticipate, though out 2019, HIPAA-compliant voice and chatbot applications for healthcare will gain prominence as these tech titans aggressively compete on voice solutions. However, the current maturity of voice technologies makes it suitable for limited voice-enabled applications such as quick medical scribes and transcription speech-based guided interactions, but not well-suited to conveying lengthy pieces of information. Moving forward, bringing voice technology to vetted clinical use cases such as elderly care, chronic condition management, physician’s assistant will provide growth opportunities.

Prediction #7: Blockchain move from Hype to Real Initial Commercial Implementations generating ROI 
During 2019 blockchain will move from perceived hype to early commercial deployment especially demonstrating initial ROIs across enterprise B2B focused initiatives. We anticipate, by end of 2019, 5%-10% of healthcare-focused enterprise blockchain applications will move from pilot stage to partial/limited commercial availability. Companies such as Change Healthcare, Hashed Health, and Guardtime among others will continue to expand their already commercial use cases. Further, a selective churn of healthcare ICOs and startups’ proof-of–concepts will finally yield few promising use cases going commercial by end of 2019. This will lure early adopters who have waited to finally jump on board creating the much-needed network effect in healthcare space. The debate will now move onto the topic of adoption exploring HOW and WHERE can blockchain technology be used in the healthcare space.

Prediction #8: Innovative Private Insurance Models Shake up Healthcare Payer Industry 
There is no denial that health insurance policies available today are aged and often fail to meet the personalized needs of individuals. As a result, the health insurance industry is expected to see less than 1.5% growth during 2018. To ensure future growth globally a number of insurance companies are already providing data and digital-driven healthcare services to their policyholders to personalize the experience and reduce the cost from potential claims. Entailing this we believe, 5-10% of health insurance plans will be linked to lifestyle and health data-driven interactive policies in some form by end of 2019. Frost & Sullivan research suggests that interactive policy will continue to gain popularity globally as it enables insurance companies to leverage individual data and then uses it to personalize premiums and discounts/rewards.
Doctor Touching Data Block In Medical Blockchain : Stock Photo
2019 will definitely be an exciting year for healthcare; be prepared for some major transformations and new entrants in the market! Watch our webinar on-demand 2019 Healthcare Predictions – Growth Opportunities, Technology, and Trends. If you would like to receive further insights on Frost & Sullivan research “Global Healthcare Industry Outlook, 2019”, connect with us at  [email protected] .

This article was written with contributions from Kamaljit Behera, Visionary Innovation Industry Analyst in Frost & Sullivan’s Transformational Health Practice.


Author
Reenita Das
I am a partner and Senior Vice President of Healthcare and Life Sciences at Frost & Sullivan, a global growth consulting and research firm. With over 25 years of healthcare consulting experience spanning numerous countries in Asia Pacific, Latin America and Eastern Europe, I have developed a passion for bringing best practices and shared learnings to other parts of the world. I also focus on looking at reverse innovation, issues of convergence and new business models. Today, much of my work is identifying the disruptions, transformation and collapses in the healthcare ecosystem in wide ranging countries and the implications of this for stakeholders. I spend my free time practicing yoga, travelling and learning from other cultures. A passion of mine includes mentoring and inspiring young women to take on leadership roles and focus of STEM based careers.
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Hospital Information System The two sides of a coin by Dr. Paridhi Mathur



Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. 

The purpose of optimization is to achieve the “best” design relative to a set of prioritized criteria or constraints. These include maximizing factors such as productivity, strength, reliability, longevity, efficiency, and utilization. 

A main cause is the deep disparities in access to care and health outcomes. Optimization methods can be used to improve the distribution and supply of health care providers to maximize service coverage, minimize travel needs of patients, limit the number of facilities, and maximize health or access equality. 

Advantages:


1. Easy Access to Patient Data 
A well-implemented Hospital Information System (HIS) means readily available patient data to the care providers. It is only a matter of few clicks and all the requisite information about a patient, from various departments in the hospital, can be available on the screen. If the treating doctor needs to re-check the test reports of a patient, she need not go looking for the IPD file; logging into the HIS will give her instant access to those reports and timely treatment decisions ensue. 

2. Cost Effective 
HIS, when implemented well, cuts out on a lot of manual work that are essentially performed in hospitals, especially the ones where documentation and record keeping is required. It helps in cutting down manpower because a lot of work gets automated and does not require manual intervention to store or analyze the information. It also saves much on storage and the related costs. 

3. Improved Efficiency 
Processes automated using software would mean that the processes will be taken care of mechanically without any human intervention and this will instantly ensure improved efficiency. The software will not face human problems like fatigue, miscommunication or lack of focus; it will perform every task assigned to it with the same accuracy day in and day out. 

4. Reduces Scope of Error 
Because processes on HIS are automated and a lot of tasks are assigned to the software to perform with utmost accuracy with minimum human intervention, the scope of error is reduced dramatically. For instance, while billing an IPD patient for the drugs used with HIS, the bill can hardly go wrong because the drug the nurse indents is what is billed for until and unless there is a shortage in stock or change in drug order after the indent has been sent. Per unit rate of the drug is saved in the software as part of standard operating procedure of automation. Just selecting the drug name and the quantity will enable the software to calculate the amount due, accurately. 

5. Increased Data Security & Retrieve-ability 
Record keeping in hospitals is a mandatory bane with two challenges: keeping the data safe with only authorized personnel getting access to it and retrieving it in the minimum possible time. Add to these the perennial problems of space shortage, protection from natural elements and protection from pest damage etc. 

HIS is the perfect solution for these problems. All the data is stored on the server or cloud, keeping it safe. Since HIS works on logins, data security is becomes a non-issue offering data access based on the role of the person – Receptionist, doctor, nurse, radiologist etc. Retrieve- ability of data stored on a server or cloud is only a matter of few clicks and the data will appear on the screen within seconds. 

6. Improved Patient Care 
Improved access to patient data and improved work efficiency means better and faster clinical decisions. In this age of evidence based medicine, the faster the clinician gets the diagnostic reports and the quicker her orders are implemented the faster is the patient recovery and the better it is on the patient care index. With automation, all departments in the hospitals are inter- connected and the faster information access further improves the quality of patient care and the resultant bed turnover in the hospital. 

HIS is more than an IT solution, it helps you offer clear information, rapidly for better patient care while ensuring that the hospital operates efficiently and improved profitability by plugging revenue leakage. Additionally, an excellent complementary solution to an HIS is a hospital insurance claims management solution to streamline the way your hospital manages patient insurance claims and settlements. 

Disadvantages:

1. Expensive 
An increasingly sophisticated health technology definitely does not come cheap. We have to understand that all first world national healthcare systems face a range of challenges; one of which is the ageing population. People are living longer. So what does this imply? This means an increased health needs but the working population generating income to pay for healthcare system is reduced. So one consideration would be, is the high cost which comes with high technology economically viable for the government? 

2. Requires fast time to Adapt 
As we know, technology is constantly evolving. Many a time there will be new softwares, new upgrades, new way of doing things. In order to keep up with the competitive edge, hospital staff has to keep up with such changes. This can be a struggle for some, especially for the older staff. 

3. Over-Dependency on Technology 
While once the staff has adapted to the new way of work, there comes the next problem. It is not uncommon for a computer system to face technical errors. The health care informatics system is no exception. This problem is especially crucial in the Accident & Emergency (A&E) Department. Various departments in the hospital are interconnected by a common information system. When one department is down, others are affected. For example, a patient was rushed into the A&E Department. When there is an error while retrieving blood analysis information, the rest of the procedures following it will be delayed. This will cause huge inconveniences, or worse; it may even have adverse effects in the patient’s health condition. 

4. Susceptibility of Network Hackers 
Patients’ medical history and other health information should be kept confidential for ethical and legal reasons. While the health care system network is definitely equipped with security measures, it is not impossible for network hacking to occur. Hence, this is certainly a vulnerability of Health Informatics. 



Author
Dr. Paridhi Mathur
TISS, Mumbai
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How to #Innovate for Impact? by Devmalya Sarkar @DevmalyaS

With digital technology advancing at warp speed or at the speed of startups these days, there's probably little that health-tech and digital health innovations can't potentially transform.


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WORKSHOP on Introduction to #EHR Standards: by CDAC and @mgumstjpr by Bipin Rathod, @bipin4uk

Register here



National Resource Centre for EHR Standards (NRCeS), C-DAC Pune and Mahatma Gandhi University of Medical Sciences & Technology, Jaipur is organizing "Workshop on Introduction to EHR Standards" at MGUMST, Jaipur on Saturday, March 02, 2019. This workshop will encourage utilization of EHR Standards notified by MoH&FW. 

Register here


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#EHR in India: Challenges and Opportunities vis-a’-vis’ Ayushman Bharat by Dr. Oommen John, @oommen_john

As India is embarking on a journey towards providing Universal Health Coverage through multi-pronged approaches of reducing catastrophic out of pocket expenditure and increasing access to essential health services , it is envisaged that Health Information Technologies (HIT) / Digital Health would create enabling environments for addressing some of the system level challenges in healthcare delivery.


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Algorithms in #EMR by Dr. Joyoti Goswami @Joyoti10




Practicing physicians these days are barraged with a lot of technical jargon promoted by the Information technology professionals such as Big Data, Hadoop, Artificial Intelligence and Predictive analytics. For a physician not introduced to the these terms, the conversation is of little value unless there is a specified value in the clinical setting.


Pulitzer Prize winning author Siddhartha Mukherjee in his book ‘Emperor of Maladies’ has quoted “The greatest clinicians who I know seem to have a sixth sense for biases. They understand, almost instinctively, when prior bits of scattered knowledge apply to their patients—but, more important, when they don’t apply to their patients.”

So the accumulation of all the 6th sense of multiple physicians in the form of scattered notes in documents is the food for Big Data professionals, who curate this data manually with standard vocabularies and then analyse patterns that can help physicians make informed decisions at the point of care.

While Predictive Analytics is a huge winner if there is genomic data available, the fact remains that in current clinical scenarios, complete genomic sequencing is not yet the norm. They are still restricted to the areas of research as the cost of doing a complete sequencing of the genome is still about $ 1000 and very few institutions are prescribing it as a norm in routine clinical practice. Once that is in place, it can lead to a whole lot of practical applications and use cases making the use of technology like Big Data and Hadoop. 

Till then, use cases which use traditional computing powers like SQL and simple queries and algorithms running on top of it could be used to get the benefits of clean data and technology in the clinical workplace.

In an attempt to utilize Predictive Analytics and Artificial Intelligence in the world of EMR and healthcare data, a list of 5 practical clinical use cases and 5 administrative/Claims Related use cases of how data could be used at the point of care and integrated with the EMR is listed out below:

Clinical Usecases

1 Acute Kidney Injury

According to Statistics, 1.2 million people per year get AKI during a hospital stay and 300,000 people in the US die annually due to AKI, this is more than breast cancer, prostate cancer, heart failure and diabetes combined. 

The successful documentation and implementation of the AKI algorithm published by the NHS is a systematic and step by step program that can help reduce the incidence of Acute Kidney Injury and the mortality and comorbidities associated with it. The e-alert for AKI installed in the LIMS (Laboratory Information Management Systems) alerts physicians when a patients’ Serum Creatinine rises greater than 26 mmol/litre from a baseline within 48 hours or there is a rise of 50% or more in 7days and/or the urine output is < 0.5 ml/kg body weight/hour. https://www.england.nhs.uk/wp-content/uploads/2014/06/psa-aki-alg.pdf

2 Sepsis Management

Sepsis or SIRS (Systemic Inflammatory Response Syndrome) accounts for 20 to 30% hospital deaths and $15.4 billion in annual healthcare costs. Needless to say early diagnosis and treatment is critical to optimal care. 

In a study, it was found that 24% of infected patients with 2 or 3 qSOFA (or Quick Sepsis Related Organ Dysfunction Assessment) points accounted for 70% of the deaths. An automated EMR based Sepsis identification system helps to detect cases with sepsis. A sepsis sniffer algorithm in ED identifies patients who exhibit at least 2 of the 3 criteria of Altered Mental Status, Fast Respiratory Rate and Low Blood Pressure and initiates the Sepsis Order set if the results of the score warrant it. The content on the Sepsis Order Set helps to make sure that none of the key variables for the management of sepsis is missed out.
https://www.mdanderson.org/documents/for-physicians/algorithms/clinical-management/clin-management-sepsis-management-adult-web-algorithm.pdf

3 Initiation of Statins

Mayo clinic has implemented a single click decision support tool within the EMR to automate the calculation of 10-year atherosclerotic   cardiovascular risk and populate the statin choice decision.  Based on the risk score calculation parameters of the ASCVD (atherosclerotic cardiovascular disease), the tool populates whether the patient needs a statin or not and if yes, which statin would be the best choice. This is defined on the basis of an algorithm running which has historical data of similar patients along with their outcomes.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157431/

4 Algorithm to identify major cardiac adverse events while on statins

Cardiovascular disease is a leading cause of death worldwide and statins are largely prescribed for the same. A study was conducted using historical data to identify patients with MACE (Major Adverse Cardiac Events) while on statins for primary prevention.  This algorithm achieved a 90 to 97% positive predictive value for the identification of adverse cardiac events while on statins
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333709/

5 Detection of Diabetic Retinopathy

Diabetic retinopathy is the fastest growing cause of blindness and screening patients with diabetes for retinopathy has been mandated as part of all the Quality Measures such as MIPS, HEDIS and others. Google analysed a dataset of  128,000 images of the retinal fundus and used that to predict retinopathy based on the lesions present on the scan such as microaneurysms, haemorrhages, exudates etc. A blindfold comparison of the results predicted by the AI algorithm was compared with the results given by experienced ophthalmologists. The AI algorithm had an accuracy of 90% which is a huge win especially in areas where there is a shortfall of specialists to interpret the results.

In the areas of diagnostics especially interpretation of Imaging results, a lot of good work is going on and soon AI capabilities will lead decision making in the interpretation of reports.
https://ai.googleblog.com/2016/11/deep-learning-for-detection-of-diabetic.html

Physicians working closely with Data scientists can help create some exciting algorithms given that healthcare centres today have large volumes of data sitting in their EMRs. A physician with even 50 patient records of 2 years has sufficient information to derive useful insights on predicting interesting patterns on the future health and preventive measures therefore can be planned accordingly. The approach for healthcare institutions should be to first identify their most pressing problems and then evaluate if any kind of Prediction of solutions to those problems could lead to better outcomes in patient care. A well thought and organized approach could go a long way to develop algorithms that work well for the hospitals and ambulatory clinics to achieve their goals and have favourable outcomes for both the patient and the hospital.

Administrative Use Cases:

In Patient Registration systems and billing systems, the algorithms can have a different flavour, some of which are listed below:

1. Healthcare Utilization Management: Integration of hospital resource utilizations in different departments, risk management and quality assurance into a management dashboard in order to ensure the judicious use of the facility's resources. A review of the procedures and services rendered by the hospital and the resources used by them (such as rooms, timings, instruments, admin and clinical staff) can help to identify under utilized and spare capacity of the hospital. This can be inbuilt in the system with the help of algorithms so as to get an optimal effect.

2. Capturing Missed and Incorrect Charges: Many facilities lose revenue due to billing errors done manually. Billing amounts associated with each DRG (Diagnosis Related Group) can be used to create models and compare them with the actual billing. Outliers in patient invoices can help to identify the likelihood of missing or incorrect charges. The higher prioritized invoices can then be reviewed to confirm the charges, which may be incorrect, over billed or under billed.

3. Predicting denials: There is a constant tug of war between the providers and payers about the claims to be settled. Most of the denials are due to incorrect documentation or missing information, duplicate claims, service already paid as a part of bundled services or others, services not covered by payer and late submission of claims. All of the above can easily be avoided by streamlining coding and billing processes. The industry benchmark for medical billing denials is 2% and in practices between 5% to 10%. Reworking on denials has a cost associated, so having algorithms running within the systems can help to streamline these efforts.

4. Predicting wait times and No shows: Idle time in clinics, both for patients and providers can be frustrating. In departments where procedures are done, it is possible to predict treatment durations by looking at historical data. Similarly the number of no shows of scheduled appointments can be predicted by including variables like age of patient, severity of disease, previous appointment regularity of the patient and others. This could help the facility to take in additional patients on the free slots proactively.

5. Measuring Bad debt, Days in A/R and DNFB (Days in Total Discharged Not Final Billed): These metrics help to determine the effectiveness of the claims generation process and effectiveness of collection efforts.

A number of KPIs tracked by hospitals and payer systems can be in built within the Revenue Management Systems and Scheduling systems. These include patient costs, ROI of the facility, Average Patient Wait times, Average patient appointment scheduling times and Patient Satisfaction.

With the healthcare market growing and many EMRs, both locally created and branded companies in the play, it is eventually the content within the EMR that can make it user friendly. Many content companies offer excellent standardised algorithms and content that can be integrated within the EMR, but there always remains physician preferences based on specialty, region of practice and other personal preferences. The end user i.e. the physician ultimately needs to decide on what suits his practice and then have the algorithms accordingly tailored within the EMR.

Author
Dr. Joyoti Goswami
Healthcare expertise of over 20 + years. Clinician having worked in specialty hospitals as medical officer and currently in the Healthcare Information Technology domain since the last 10 + years. Have a good combination of clinical and technology skills in multiple areas. Worked with multiple EMRs such as Allscripts, GE Centricity, Athena health and Nextgen
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#RPA in Healthcare: The Path Ahead for Health IT Leaders By Sreejith Madhavan



Historically, healthcare industry has shown a reluctance to invest in technologies that did not come under the purview of diagnostics and treatment, or demanded by insurance payors (such as electronic claims submission). Anything that required cognitive (human) intervention or intuition was kept aside from the technological takeover. The unprecedented growth of life expectancy, the discovery of new drugs and treatments, and the ability of modern medicine to combat chronic ailments and epidemics have spurred the need for technological inclusion in multiple areas of healthcare.

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