Comparison of #telemedicine with in-person care for follow-up after elective neurosurgery: results of a cost-effectiveness analysis of 1200 patients

Comparison of telemedicine with in-person care for follow-up after elective neurosurgery: results of a cost-effectiveness analysis of 1200 patients using patient-perceived utility scores

Authors

Sumit Thakar, MCh,1 Niranjana Rajagopal, DNB,1 Subramaniyan Mani, MTech,2 Maya Shyam, PGDM,3 Saritha Aryan, MS, MCh,1 Arun S. Rao, DNB,1 Rakshith Srinivasa, MCh,1 Dilip Mohan, MS, MCh, DNB,1 and Alangar S. Hegde, MCh, PhD 1Department of Neurological Sciences, 2Hospital Management Information System, and 3Finance and Accounts, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore, India

OBJECTIVES

The utility of telemedicine (TM) in neurosurgery is underexplored, with most of the studies relating to teletrauma or telestroke programs. In this study, the authors evaluate the cost-effectiveness of TM consultations for followup care of a large population of patients who underwent neurosurgical procedures.

METHODS

A decision-analytical model was used to assess the cost-effectiveness of TM for elective post–neurosurgical care patients from a predominantly nonurban cohort in West Bengal, India. The model compared TM care via a nodal center in West Bengal to routine, in-person, per-episode care at the provider site in Bangalore, India. 

Cost and effectiveness data relating to 1200 patients were collected for a 52-month period. The effectiveness of TM care was calculated using efficiency in terms of the percentage of successful TM consultations, as well as patient-perceived utility values for overall experience of the type of health care access that they received. 


Incremental cost-effectiveness ratio (ICER) analysis was done using the 4-quadrant charting of the cost-effectiveness plane. One-way sensitivity and tornado analyses were performed to identify thresholds where the care strategy would change.

RESULTS

The overall utility for the 3 TM scenarios was found to be higher (89%) than for the utility of routine care (80%). TM was found to be more cost-effective (Indian rupee [INR] 2630 per patient) compared to routine care (INR 6848 per patient). 

The TM strategy “dominates” that of routine care by being more effective and less expensive (ICER value of -39,400 INR/unit of effectiveness). Sensitivity analysis revealed that cost-effectiveness of TM was most sensitive to changes in the number of TM patients, utility and success rate of TM, and travel distance to the TM center.

CONCLUSIONS

TM care dominates the in-person care strategy by providing more effective and less expensive follow-up care for a remote post–neurosurgical care population in India. In the authors’ setting, this benefit of TM is sustainable even if half the TM consultations turn out to be unsuccessful. The viability of TM as a cost-effective care protocol is attributed to a combination of factors, like an adequate patient volume utilizing TM, patient utility, success rate of TM, and the patient travel distance.

Link to Full Article : https://thejns.org/doi/abs/10.3171/2018.2.FOCUS17543


Link to VIDEO Presentation by the Authors: https://vimeo.com/262374146


The Article has been published earlier and is re-published here with the author’s permission 

AUTHORS

Sumit Thakar: Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore, India | https://www.linkedin.com/in/sumit-thakar-25748b12/

Subramanyan Mani, Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore, India. https://www.linkedin.com/in/subramaniyan-m-22583618/ 


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Why India needs Healthcare Information Technology (HIT) by Dr Pramod D. Jacob


India with its vast population of over 1.3 billion firstly has a challenge in keeping a track of this vast population’s health, much less keep them healthy.  One of the major reasons for this is lack of timely, accurate and reliable healthcare information in today’s paper world

State of Health in India

In healthcare India ranks very poorly, even compared to our neighbouring countries. For example in the following health indicators: –

Maternal Mortality Rate (year 2015): defined as number of women who die during pregnancy and childbirth, per 100,000 live births. India has a rate of 174 maternal deaths per 100,000 live births, which is worse than Bhutan (148 /  100,000) or Sri Lanka (30 / 100,000 ). China which also has a large population is much better (27 / 100,000) 

Infant Mortality Rate (year 2017): defined as number of children who die less than one year of age per 1000 live births. In India the figure is 39 per 1000 live births, behind Bangladesh ( 32 / 1000 ) and Nepal ( 28 / 1000 ). China is 12 / 1000.

State of healthcare information collection for events like epidemics in India

Before 2010, it would take about six months for the health information to be collected, collated and analysed to prove that a given region in India had an epidemic as the entire process was paper based. By that time the disease (with most being self-limiting) would have struck, had its toll of morbidity and mortality and run its course. With most data collection being paper based this delay costs India loss of lives and productivity with high morbidity, especially in rural areas ( in urban areas- private hospitals and clinics have a process of notifying the public health authorities for notifiable diseases, hence epidemics are identified earlier in urban areas) .

To top it all there is general disbelief in the official published health statistics in India. For example, official data claimed that Malarial deaths in India was only 1,023 in 2010, however a Lancet published study showed the figure to be actually 46,800. Following the Lancet article, the official data agreed that they had their figures off by twenty to thirty times.  Even for a common disease like Cholera, which strikes every monsoon in endemic areas along the Ganges and Brahmaputra, the official estimate for India is 3,631 cases per year, while research has shown this to be about 22,200 per year.   

While the immediate reaction is to blame the public health authorities and Government in India, one must understand the limitations in a paper world to collect health information of 1.3 billion people across 3,200,000 square kilometres. Compare that to collection of information electronically – an electron can travel around the world in about 19 seconds. 

The solution – Healthcare Information Technology (HIT)

The solution is to produce healthcare information in a timely manner with accuracy and reliability. To achieve speed, it is best to do so with Information Technology – hence HIT. To achieve accuracy and reliability, it is best if the patient’s data is put into the HIT system by the providers of healthcare such as doctors, nurses, pharmacist etc at the point of care. This patient level data can then be collated and processed to get timely, accurate and reliable population-based healthcare information.

 In addition, HIT systems provides the power of IT to healthcare such as giving alerts for drug-drug interactions, duplication in lab tests and bringing about efficiency in processes and workflows in a healthcare setting, producing reports quickly which will help in planning and deployment of healthcare. It is estimated that healthcare doubles in knowledge every few months and it is difficult for doctors to keep up. With HIT it will be possible to keep up with the latest and deploy best practice evidence-based medicine applicable for India.

The proof of HIT bringing exponential improvement in speed and access to important healthcare information like epidemics even in Indian public health, is best exemplified by the IDSP program. The IDSP program has gone digital from district level upwards to state and then to the National Centre for Disease Control (NCDC), Delhi. As a result, the NCDC now publishes data on epidemics and events on a month to month basis and will soon be publishing it on a weekly basis. Will cover the details of this program in a future write up. 

This article has been republished here with the author’s permission. The article was first published here.

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

After completing his medical degree from CMC Vellore and doing his Master of Science in Medical Informatics from Oregon Health Sciences University (OHSU) in the US, Dr Pramod worked in the EMR division of Epic Systems, USA and was the Clinical Systems Project Manager in Multnomah County, Portland, Oregon. He has been a Healthcare Information Technology consultant to Benton County, Oregon and Santa Cruz County, California. In 2007 he relocated to India and did consultancy work for the state governments of Tamil Nadu and Himachal Pradesh. He was a member of the HIMSS Global EHR Task Force and the lead for India in the task force.

At present he is the Chief Medical Officer of dWise Healthcare IT solutions, involved in the designing and implementation of Clinical Information Systems and the EHR for the company. He is also a consultant for WHO India in the IDSP project and for PHFI for a Non Communicable Diseases Decision Support Application.

How can Digital Health be Implemented as envisaged in the National Health Policy 2017? by Prof. Supten Sarbadhikari @supten


The National Health Policy 2017 (NHP-2017) of India correctly identified the need for creating many new institutions like the National Digital Health Authority (NDHA).  However, the ground realities don’t appear to have been considered well enough. Early setting up of a functional NDHA is essential for India to avoid a digital health mess in future. The first job for the proposed NDHA will be to formulate a robust National Digital Health Strategy / Policy, in consultation with all the stakeholders. Caution needs to be exercised before cross referrals and sharing disparate information among different systems of medicine. Health informatics education must be embedded as an integral part for health and hospital management. It may be prudent to include Health in the Concurrent list of the Constitution of India. That will ensure a smooth adoption of digital health in India. Seeking comments on the Draft Bill DISHA (Digital Information Security in Healthcare Act) is a good start.


It has been now more than a year that the (third edition of the) National Health Policy 2017 (NHP-2017) of India has been notified. While the accompanying Situation Analysis didn’t mention anything about Digital Health, the Policy correctly identified the need for creating many new institutions like the National Digital Health Authority (NDHA).

Now, let us look at where do we stand one year later, regarding the ushering in of Digital Health in India.

First let us glance at some of the key provisions of the NHP-2017 as mentioned in the various sections. Just beneath the quotes from the relevant sections of the NHP-2017, I’m commenting on certain issues for thought.

2.4.3.3 Health Management Information

a. Ensure district-level electronic database of information on health system components by 2020.
b. Strengthen the health surveillance system and establish registries for diseases of public health importance by 2020.
c. Establish federated integrated health information architecture, Health Information Exchanges and National Health Information Network by 2025.

Comments: The NHP-2017 focuses on Digital technology, right from the beginning. Some timelines are also proposed here. However, while some states have been doing very well, some others are lagging. We would further elaborate on this aspect towards the end.

3.3 Organization of Public Health Care Delivery: 
For effectively handling medical disasters and health security, the policy recommends that the public healthcare system retain a certain excess capacity in terms of health infrastructure, human resources, and technology which can be mobilized in times of crisis.

In order to leverage the pluralistic health care legacy, the policy recommends mainstreaming the different health systems. This would involve increasing the validation, evidence and research of the different health care systems as a part of the common pool of knowledge. It would also involve providing access and informed choice to the patients, providing an enabling environment for practice of different systems of medicine, an enabling regulatory framework and encouraging cross referrals across these systems.

Comments: Here there is a need for more caution since the other streams of medicine – viz., Ayurveda, Yoga and Naturopathy, Siddha, Unani and Homeopathy, follow entirely different principles from those followed by modern medicine. Therefore, cross referrals may add to the complexity and confusion, ultimately harming the patient.

11.1 Medical Education: 
The policy recognizes the need to revise the under graduate and post graduate medical curriculum keeping in view the changing needs, technology and the newer emerging disease trends.

Comments: There have been a lot of issues regarding the Medical Council of India and the National Board of Examinations in the past, followed by a proposed revamping through the National Medical Commission. Despite all the proposed changes, one of the essential features that is amiss is the incorporation of health informatics essentials in all branches of health professional education. Without doing that, a smooth adoption of digital health is extremely difficult.

11.8 Public Health Management Cadre: 
The policy proposes creation of Public Health Management Cadre in all States based on public health or related disciplines, as an entry criteria.

Comments: In continuation of the previous section, health information management must be embedded as an integral part for health and hospital management. Health Informatics weds both health information technology and health information management. Scaling up, public health informatics combines health informatics and population demographics.

13.12: Health Information System: 
The objective of an integrated health information system necessitates private sector participation in developing and linking systems into a common network/grid which can be accessed by both public and private healthcare providers. Collaboration with private sector consistent with Meta Data and Data Standards and Electronic Health Records would lead to developing a seamless health information system. The private sector could help in creation of registries of patients and in documenting diseases and health events.

Comments: Most of the times various health information systems don’t talk to each other and therefore there is a dire need of Standards for interoperability. I would discuss this issue in greater details this issue towards the end, where I would talk about the Clinical Establishments Act.

14.2: Regulation of Clinical Establishments: 
A few States have adopted the Clinical Establishments Act 2010. Advocacy with the other States would be made for adoption of the Act. Grading of clinical establishments and active promotion and adoption of standard treatment guidelines would be one starting point. Protection of patient rights in clinical establishments (such as rights to information, access to medical records and reports, informed consent, second opinion, confidentiality and privacy) as key process standards, would be an important step. Policy recommends the setting up of a separate, empowered medical tribunal for speedy resolution to address disputes /complaints regarding standards of care, prices of services, negligence and unfair practices. Standard Regulatory framework for laboratories and imaging centers, specialized emerging services such as assisted reproductive techniques, surrogacy, stem cell banking, organ and tissue transplantation and Nano Medicine will be created as appropriate.

Comments: Discussed below separately.

14.5: Medical Devices Regulation: 
The policy recommends strengthening regulation of medical devices and establishing a regulatory body for medical devices to unleash innovation and the entrepreneurial spirit for manufacture of medical device in India. The policy supports harmonization of domestic regulatory standards with international standards. Building capacities in line with international practices in our regulatory personnel and institutions, would have the highest priority. Post market surveillance program for drugs, blood products and medical devices shall be strengthened to ensure high degree of reliability and to prevent adverse outcomes due to low quality and/or refurbished devices/health products.

Comments:  Medical Devices Rules, 2017 that has come into force with effect from 1st day of January, 2018, has included in the Part-I of the first schedule Parameters for classification of medical devices other than in vitro diagnostic medical devices. There, Software as Medical Device (SaMD) is defined as: (iii) Software, which drives a device or influences the use of a device, falls automatically in the same class. This is indeed a very forward looking and welcome legislation, ahead of the times in our country.

22: Health Technology Assessment: 
Health Technology assessment is required to ensure that technology choice is participatory and is guided by considerations of scientific evidence, safety, consideration on cost effectiveness and social values. The National Health Policy commits to the development of institutional framework and capacity for Health Technology Assessment and adoption.

Comments: We can combine these aspects with the digital health technology, described in the next section.

23: Digital Health Technology Eco – System: 
Recognising the integral role of technology(eHealth, mHealth, Cloud, Internet of things, wearables, etc) in the healthcare delivery, a National Digital Health Authority (NDHA) will be set up to regulate, develop and deploy digital health across the continuum of care. The policy advocates extensive deployment of digital tools for improving the efficiency and outcome of the healthcare system. The policy aims at an integrated health information system which serves the needs of all stake-holders and improves efficiency, transparency, and citizen experience. Delivery of better health outcomes in terms of access, quality, affordability, lowering of disease burden and efficient monitoring of health entitlements to citizens, is the goal. Establishing federated national health information architecture, to roll-out and link systems across public and private health providers at State and national levels consistent with Metadata and Data Standards (MDDS) & Electronic Health Record (EHR), will be supported by this policy. The policy suggests exploring the use of “Aadhaar” (Unique ID) for identification. Creation of registries (i.e. patients, provider, service, diseases, document and event) for enhanced public health/big data analytics, creation of health information exchange platform and national health information network, use of National Optical Fibre Network, use of smartphones/tablets for capturing real time data, are key strategies of the National Health Information Architecture.

23.1 Application of Digital Health: 
The policy advocates scaling of various initiatives in the area of tele-consultation which will entail linking tertiary care institutions (medical colleges) to District and Sub-district hospitals which provide secondary care facilities, for the purpose of specialist consultations. The policy will promote utilization of National Knowledge Network for Tele-education, Tele-CME, Tele-consultations and access to digital library.

23.2 Leveraging Digital Tools for AYUSH: 
Digital tools would be used for generation and sharing of information about AYUSH services and AYUSH practitioners, for traditional community level healthcare providers and for household level preventive, promotive and curative practices.

Comments: This is a very correct decision and the first job for the proposed NDHA will be to formulate a robust National Digital Health Strategy / Policy, in consultation with all the stakeholders. The first constituents of the Authority will lay down the rules of the game as to how will digital health be adopted in India. The earlier the NDHA is set up and functional, the better it will be for India to avoid a digital health mess in future. Any delay in the process might make us deal with non-interoperable legacy systems, as has been the case in many developed nations. However, cross referrals and sharing disparate information among different systems of medicine may add to the complexity and confusion, ultimately harming the patient. Currently, the MoHFW is seeking comments on the proposed DISHA (Digital Information Security in Healthcare Act) that will be the Bill setting up the NDHA / NeHA.

25. Health Research: 
The National Health Policy recognizes the key role that health research plays in the development of a nation’s health. In knowledge based sector like health, where advances happen daily, it is important to increase investment in health research.

25.1 Strengthening Knowledge for Health: 
The policy envisages strengthening the publicly funded health research institutes under the Department of Health Research, the apex public health institutions under the Department of Health & Family Welfare, as well as those in the Government and private medical colleges. The policy supports strengthening health research in India in the following fronts- health systems and services research, medical product innovation (including point of care diagnostics and related technologies and internet of things) and fundamental research in all areas relevant to health- such as Physiology, Biochemistry, Pharmacology, Microbiology, Pathology, Molecular Sciences and Cell Sciences. Policy aims to promote innovation, discovery and translational research on drugs in AUSH and allocate adequate funds towards it. Research on social determinants of health along with neglected health issues such as disability and transgender health will be promoted. For drug and devices discovery and innovation, both from Allopathy and traditional medicines systems would be supported. Creation of a Common Sector Innovation Council for the Health Ministry that brings together various regulatory bodies for drug research, the Department of Pharmaceuticals, the Department of Biotechnology, the Department of Industrial Policy and Promotion, the Department of Science and Technology, etc. would be desirable. Innovative strategies of public financing and careful leveraging of public procurement can help generate the sort of innovations that are required for Indian public health priorities. Drug research on critical diseases such as TB, HIV/AIDS, and Malaria may be incentivized, to address them on priority. For making full use of all research capacity in the nation, grant- in- aid mechanisms which provide extramural funding to research efforts is envisaged to be scaled up.

25.2 Drug Innovation & Discovery: 
Government policy would be to both stimulate innovation and new drug discovery as required, to meet health needs as well as ensure that new drugs discovered and brought into the market are affordable to those who need them most. Similar policies are required for discovering more affordable, more frugal and appropriate point of care diagnostics as also robust medical equipment for use in our rural and remote areas. Public procurement policies and public investment in priority research areas with greater coordination and convergence between drug research institutions, drug manufacturers and premier medical institutions must also be aligned to drug discovery.

25.3 Development of Information Databases: 
There is also a need to develop information data-bases on a wide variety of areas that researchers can share. This includes ensuring that all unit data of major publicly funded surveys related to health, are available in public domain in a research friendly format.

25.4 Research Collaboration: 
The policy on international health and health diplomacy should leverage India’s strength in cost effective innovations in the areas of pharmaceuticals, medical devices, health care delivery and information technology. Additionally leveraging international cooperation, especially involving nations of the Global South, to build domestic institutional capacity in green-field innovation and for knowledge and skill generation could be explored.

Comments: For health research and innovation the government’s role of encouraging Standards for interoperability and allowing open data for analysis will go a long way.
Apart from the NHP-2017, there are certain existing legislations that affects the adoption of digital health in India. The first and foremost is the 2012 Amendments of the Clinical Establishments Act 2010. The other guidance comes from the Constitution of India. Both of these are discussed below.

Clinical Establishments (Registration and Regulation) Act (CEA): 
In 2012, the MoHFW amended the CEA (2010) and added Clause “9 (iv): the clinical establishments shall maintain and provide Electronic Medical Records (EMR) or Electronic Health Records (EHR) of every patient as may be determined and issued by the Central Government or the State Government as the case may be, from time to time”.

Comments: The Act has taken effect in the four states namely, Arunachal Pradesh, Himachal Pradesh, Mizoram, Sikkim, and all Union Territories since 1st March, 2012 vide Gazette notification dated 28th February, 2012. The states of Uttar Pradesh, Uttarakhand, Rajasthan, Jharkhand, Bihar and Assam have adopted the Act under clause (1) of article 252 of the Constitution. 

The Ministry has notified the National Council for Clinical Establishments and the Clinical Establishments (Central Government) Rules, 2012 under this Act vide Gazette notifications dated 19th March, 2012 and 23rd May, 2012 respectively.

The Act is applicable to all kinds of clinical establishments from the public and private sectors, of all recognized systems of medicine including single doctor clinics. The only exception will be establishments run by the Armed forces.


The good point is the enactment of the necessity for EMR / EHR. The Ministry of Health and Family Welfare has been notifying Standards for EHR since August 2013 and the second edition of the Guidelines were notified in December 2016. That is the right way to move forward. However, Health being a State subject, not all the states are equally keen to adopt it.


Concurrent List: The seventh schedule of the Constitution of India lists “Health” (Public health and sanitation; hospitals and dispensaries) under the Item 6 of List-II (State list). As expected, like the Union ministry, health ministers of various states have also agreed to equipping PHCs and CHCs with latest technology.

Comments: However, as seen in the previous section, the CEA has not yet been adopted by most of the states of India. Therefore, although the CEA mandates EMR / EHR, most of the states are not yet bound to follow it. Since Health is neither in the Union list, nor in the Concurrent list, it may be prudent to include it in the Concurrent list. In that case adoption of digital health would be much smoother.

Conclusions:
While the NHP-2017 is bold in its thoughts and foresight, for facilitating digital health, the ground realities don’t appear to have been considered well enough. Early setting up of a functional NDHA is essential for India to avoid a digital health mess in future. Inordinate delays might make us deal with non-interoperable legacy systems. The first job for the proposed NDHA will be to formulate a robust National Digital Health Strategy / Policy, in consultation with all the stakeholders. 

Caution needs to be exercised before cross referrals and sharing disparate information among different systems of medicine. Health informatics education must be embedded as an integral part for health and hospital management. Since Health is neither in the Union list, nor in the Concurrent list of the Constitution of India, it may be prudent to include it in the Concurrent list. In that case adoption of digital health would be much smoother. 

Seeking comments on the Draft Bill DISHA (Digital Information Security in Healthcare Act) is a good start.

References:
[1]: Ministry of Health and Family Welfare, Government of India, National Health Policy 2017: https://www.nhp.gov.in//NHPfiles/national_health_policy_2017.pdf (Accessed 19th February 2018)

[2]: Ministry of Health and Family Welfare, Government of India. Situation Analyses: Backdrop to the National Health Policy – 2017, New Delhi. Available from : https://mohfw.gov.in/sites/default/files/71275472221489753307.pdf

[3]: Sundararaman T, National Health Policy 2017: A Cautions Welcome, Indian J Med Ethics. 2017 Apr-Jun;2(2):69-71

[4]: Sarbadhikari SN. A farce called the National Board of Examinations. Indian J Med Ethics. 2010 Jan-Mar;7(1):20-2

[5]: Thomas G, Medical education in India – the way forward, Indian J Med Ethics. 2016 Oct-Dec;1(4):200

[6]: Government of India, The Gazette of India, dated 31/01/2017: http://www.cdsco.nic.in/writereaddata/Medical%20Device%20Rule%20gsr78E(1).pdf (Accessed 19th February 2018)

[7]: Government of India, The Gazette of India, dated 19/8/2010, Clinical Establishments (Registration and Regulation) Act 2010:
 http://clinicalestablishments.nic.in/WriteReadData/969.pdf  (Accessed 19th February 2018)

[8]: Government of India, The Gazette of India, dated 23/5/2012, Clinical Establishments (Registration and Regulation) Act, (Amendments) 2012:
http://clinicalestablishments.nic.in/WriteReadData/386.pdf (Accessed 19th February 2018)

[9]: Ministry of Health and Family Welfare, Government of India.  http://clinicalestablishmentstraining.nic.in/cms/Home.aspx (Accessed 19th February 2018)

[10]: National Health Portal, Ministry of Health and Family Welfare, Government of India, EHR Standards: https://www.nhp.gov.in/electronic-health-record-standards-for-india-helpdesk_mty (Accessed 19th February 2018)

[11]: Government of India, The Constitution of India  http://lawmin.nic.in/olwing/coi/coi-english/coi-4March2016.pdf  (Accessed 19th February 2018)

[12]: Press Information Bureau, Government of India, Shri J P Nadda chairs 12th Conference of the Central Council of Health and Family Welfare to discuss Draft National Health Policy, dated 27/02/2016: http://pib.nic.in/newsite/PrintRelease.aspx?relid=136961 (Accessed 19th February 2018)

[13]: Ministry of Health and Family Welfare, Government of India. https://mohfw.gov.in/newshighlights/comments-draft-digital-information-security-health-care-actdisha (Accessed 28th March 2018)

[14]: National Health Portal, Ministry of Health and Family Welfare, Government of India, EHR Standards: https://www.nhp.gov.in/ehr-standards-helpdesk_ms (Accessed 28th March 2018)

The article was first published on Dr. Supten’s Blog here, its been re-published here with the author’s permission.

Author

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Prof. Supten Sarbadhikari

Digital Health Influencer & Project Director at Centre for Health Informatics of the National Health Portal; President IAMI (2016)
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Clinical Decision Support Systems: Resolving the “Build or Buy” Dilemma – Part 2 by Dr. Ujjwal Rao, @drujjwalrao


The 2 part paper (review part 1 here): Discusses the key role of evidence-adaptive clinical decision support systems (CDSS) in the healthcare system of the future. Weighs the pros and cons that hospitals should consider when deciding to buy or build such decision support tools

Healthcare providers today face the challenge of delivering up-to-date, evidence-based care given the ever burgeoning pool of medical evidence, which is not only prone to inconsistencies but also take an average of 17 years to make their way into routine clinical practice. 

Coupled with the hassle of meeting advance electronic health record (EHR) platform integration requirements, Dr. Rao proposes that buying knowledge-based CDSS is increasingly more favorable and the way forward. 

A number of major initial and ongoing hurdles with home-grown solutions – including the significant time and effort needed to constantly update evidence – could overwhelm and overburden healthcare organizations, taking time away from delivering standardized and evidence-based care. 

Dr. Rao offers five ways on how these challenges can be avoided with the purchase of third-party CDSS platforms.



Care that is important is often not delivered. Care that is delivered is often not important1.

IMPLICATIONS OF BUILDING CDSS

While the idea of building a CDSS that perfectly fits your organisation’s unique workflows appears an obvious choice, it eventually becomes clear that maintaining such a one-of-a-kind system can be unsustainable.

Medical knowledge-base construction and maintenance is a significant challenge. After the first few years of creating the knowledge base, adding new evidence to the system is no longer research – it is system development. As such, it becomes increasingly difficult to recruit a cadre of medically knowledgeable individuals who can devote substantial effort to knowledge-base maintenance over time. Creating a Clinical Practice Guideline (CPG) usually takes three to six months (or even a year), depending on the subject matter9. 

The maintenance of a CPG is likely to take more than a quarter of the time it took to originally develop the Guideline10. To develop CPGs, a standard set of guidelines covering all specialties, represents 12,000 hours of work at a cost of more than 1 million USD for just the content alone10. The total cost of authoring, reviewing, and EHR integration can surpass 3 million USD for just 200 Order Sets11. With the growth of Fifth and Sixth Generation EHRs, the concept of building in-house CDSS will increasingly become less favorable.

BENEFITS OF BUYING CDSS

As we move from logical (rules-based) CDSS to a foreseeable future of statistical (machine learning-based) systems, the decision to purchase rather than build and maintain knowledge-based CDSS becomes a sensible, convenient and cost-effective choice (Table 2).



First and foremost, choosing to “buy” third-party CDS Solutions can help to outsource the huge burden of managing and updating the clinical knowledge base to a vendor that provides the dual advantage of access to peer-reviewed content created by experts, and amalgamating organisational workflows with
evidence-based practices via collaborative platforms. Building consensus amongst “experts” becomes easier when the source of evidence is credible.

Advanced CDSS are usually built on accepted and defined standards that have been peer-reviewed and fine-tuned to provide higher sensitivity and specificity for each condition.


Customisation can also be taken a step further by selecting solutions that have a content management system for ease of customising the content to fit specific guidelines of the organisation. These external CDSS may also have a proven track record of effectiveness with other organisations, which in turn results in costs savings for less ‘trial-and-error’ as compared to “building” CDSS. The return on investment is primarily in the form of reduced spending on unnecessary tests and procedures as well as avoidance of costly adverse events (and in many systems, malpractice litigation claims), and secondly in the form of saved care replacement costs that result from pulling clinicians away from care processes (to build CDSS). These savings can add up to significant amount annually – almost 2.6 million USD as per one estimation11. 

Furthermore, such standard CDSS implementations enable interoperability in Health Information Exchanges. As far as project implementation is concerned, an external influence provides the opportunity to reengineer improvements into your original processes. Advancements in interoperability standards also facilitate more seamless integration with EHR. Professional practice services for EHR integration and implementation support that are provided by progressive knowledge partners, can cut down the implementation costs significantly and improve the efficiency and effectiveness of a large-scale CDSS roll-out. 

Lastly, with pharmacogenomics becoming an emerging field in patient care, demand for this new form of CDSS is increasing. In this case, building this knowledge base seems even less of an option when considering the expertise and time needed to manage and update it.

CONSIDERATIONS IN SELECTING CDSS


As with all third-party platforms, the convenience that comes with buying often includes challenges that need consideration. These include the integration effort for disparate platforms, investment in system upgrades and the additional effort from IT staff for monitoring the performance of these external platforms. Legacy stand-alone CDSS systems need to be integrated or discontinued. Yet, certain safeguards or mitigation plans can be considered to maximize the advantage of buying CDSS. 

These are best summarized in the following five C’s: 

1. Clinical team: Selection and implementation of the CDSS should involve the clinical teams to ensure that it meets the needs of the end-user for successful adoption and continued usage 
2. Credibility: Consider vendors with a proven case of working with other providers to smoothly manage the change experience 
3. Capability: Evaluate vendor’s ability to effectively synthesize evidence into evidence-adaptive technology platforms, thereby successfully bringing about true practice transformation 
4. Configuration: Ensure EHR platforms have been configured to integrate with CDSS and updated to comply with latest interoperability standards 
5. Computation: Define metrics that measure performance of each CDS element and outline a clear process for monitoring to tweak elements that are underperforming 

Following the appointment of a vendor, healthcare organizations should further establish governance structures and develop a clinical knowledge management framework that can consistently track and improve effectiveness of their chosen CDSS platform. In all, keeping in mind the above considerations will enable providers to better chart out their journey towards a successful CDSS adoption.

CONCLUSION

With the deluge of evidence that is often fallible and slow to diffuse into clinical practice, along with advanced EHR platform integration requirements, hospitals must reconsider their likely initial inclination towards building their own CDSS. A number of major initial and ongoing challenges with home-grown solutions, including care replacement costs, time and effort to constantly update evidence; usability; implementation and maintenance costs; and accepted functional practice integration can be overcome with the purchase of proprietary CDSS. Overall, the selection of CDSS should also involve the clinical team from the start, as well as careful selection of vendors who show a high level of willingness to partner in the transformation journey.


Author

Dr. Ujjwal Rao

Dr. Ujjwal Rao is Senior Clinical Specialist in Integrated Decision Support Solutions, and is based in New Delhi, India. He provides strategic counsel to health providers on designing world-class clinical decision support systems with Elsevier’s comprehensive suite of current and evidence-based information solutions that can improve the quality and efficient delivery of healthcare.

An experienced emergency physician, executive, clinical informaticist and technology evangelist, Dr. Rao has a decade of experience serving in trust and corporate hospitals in various roles ranging from clinical administration, hospital operations to quality & accreditation. In his former positions, Dr. Rao led EHR implementations for large hospital groups and designed bespoke healthcare analytic solutions to raise profitability.

His passion to see transformation through technology led him to volunteer as a quality consultant with the United Nations. He also currently serves as an Assessor on the Panel of the Quality Council of India for the National Healthcare Accreditation Standards body, NABH.

Dr. Rao obtained his degree in Medicine and then specialized in Hospital and Health Systems Management, Medical Law and Ethics before completing his PhD in Quality and Medical Informatics.

Clinical Decision Support Systems: Resolving the “Build or Buy” Dilemma – Part 1 by Dr. Ujjwal Rao, @drujjwalrao

The 2 part paper: Discusses the key role of evidence-adaptive clinical decision support systems (CDSS) in the healthcare system of the future. Weighs the pros and cons that hospitals should considered when deciding to buy or build such decision support tools

Healthcare providers today face the challenge of delivering up-to-date, evidence-based care given the ever burgeoning pool of medical evidence, which is not only prone to inconsistencies but also take an average of 17 years to make their way into routine clinical practice. 

Coupled with the hassle of meeting advance electronic health record (EHR) platform integration requirements, Dr. Rao proposes that buying knowledge-based CDSS is increasingly more favorable and the way forward. 

A number of major initial and ongoing hurdles with home-grown solutions – including the significant time and effort needed to constantly update evidence – could overwhelm and overburden healthcare organizations, taking time away from delivering standardized and evidence-based care. 

Dr. Rao offers five ways on how these challenges can be avoided with the purchase of third-party CDSS platforms.



Care that is important is often not delivered. Care that is delivered is often not important1.


The importance of clinical care grounded in a reliable evidence base cannot be over-emphasised. Evidence-based care processes, supported by automated clinical information and decision support systems, offer the greatest promise of achieving the best outcomes2. Proprietary Clinical Decision Support Systems (CDSS) built on evidence-adaptive platforms incorporating clinical knowledge that continually reflects current EBM gleaned from both the research literature and sources of practice expertise will soon outgrow self-synthesised (home-grown) solutions. This paper explores this process.

Clinical practice is full of contradictions, not only where individual professional experiences conflict, but even where “evidence” partially or completely disagrees. The primary reason for these inconsistencies is that evidence is dynamic and emergent, never constant.

The Fallibility of Evidence

Evidence can often be incomplete, with varying levels of quality and strength of recommendations3. Keeping up with latest evidence and eliminating its inconsistencies is quite an arduous task and carries the inherent risk of practicing outdated medicine (with occasional catastrophic consequences). 

Consider the following scenario:

A 2 month-old infant comes to your office suffering from heart failure. She has a prescription for two drugs that reduce excess fluids from the body (diuretics), prescribed by a cardiologist based on evidence demonstrating the effectiveness of the two medications when administered together. One of the drugs reduces the body’s level of potassium (an important electrolyte) while the other conserves potassium. You are doubtful that two drugs are required for the treatment of such a young patient. Given the amount of time it will take to find evidence to address your skepticism, you call the cardiologist. Unfortunately, the prescribing cardiologist is unavailable, so you then call another renowned cardiologist. He tells you to stop the second drug based on professional experience that it causes growth problems in infants as well as his belief that potassium loss is of little concern in infants. You are now left wondering what is best for your tiny patient, having moved from a stage of having no information to a stage of conflicting “information noise.”

Given such realities of evidence-based medicine, one must consider: is the business of extrapolating evidence something providers and healthcare organisations are willing to do on their own?

THE EVIDENCE DELUGE

It has been estimated that greater than two million articles are published in the biomedical literature each year4. If a physician were to attempt to keep up with this literary explosion by reading two articles each day, at the end of one year, that physician would be more than sixty centuries behind! If physicians were to read everything of possible clinical relevance, they would need to read around 6,000 articles a day! 

Compounding this problem is the conundrum of diffusion. “Diffusion” is the spread of best (research) evidence on managing diseases and symptoms to the patient bedside5. According to conventional wisdom, it takes an average of 17 years for validated clinical research findings to make their way into routine clinical practice6. In an age where global public health emergencies (like the recent Zika virus outbreak) require “knowledge hyper-loops” for rapid diffusion of knowledge into general practice, the 17-year latency needs to be radically shortened to 17 hours or even less.

A SOLUTION TO ACCELERATE LEARNING HEALTH SYSTEMS

Clinical Decision Support Systems (CDSS) have been described as the Computerised Patient Record (CPR) System’s Crown Jewel7. According to Gartner’s CPR generations (Fig 1), CPRs or Electronic Health Records (EHRs) have had an increasingly positive impact over the last few decades in reducing medical errors. With the inclusion of CDSS, the EHR evolves from being a provider “colleague” to a “mentor,” with the power to cover the entire care continuum in guiding clinicians at all points of care.



We are now seeing the evolution of the Sixth Generation EHR – “The Seer,” that has computable, standardised clinical data able to invoke clinical decision support from evidence-adaptive CDSS platforms. Although at present evidence-adaptive platforms require human intervention, we are now beginning to see the inclusion of artificial neural networks (deep learning), Bayesian networks, reinforcement learning, and other artificial intelligence techniques for synthesising evidence relevant to patient data in real-time, with potentially unprecedented insights for clinicians. Intelligence Augmentation (IA), where technology amplifies the decision-making capabilities of humans, has linked healthcare providers to vast amounts of patient data with relevant clinical knowledge, in real-time, at the point-of-care. We are likely to soon witness wide-scale proliferation of IA in Sixth Generation EHRs that incorporate evidence-adaptive CDSS. 

This kind of evidence-adaptive CDSS is at the heart of a Learning Health System (LHS)1, wherein evidence influences practice and the practice, in turn, generates evidence, creating self-propagating, virtuous cycles that bring about better, safer clinical care at optimal costs.



There are six critical success factors (Table 1) for a CDSS, based on the ACUDIR model (Latin for “Come to the Rescue”), that can form the foundation of such a rapid LHS.


CDSS solutions like Order Sets, Care Plans, and Clinical Pathways are a combination of evidence-based content and advanced technology platforms. The dilemma which healthcare organisations face today is whether they can “build” such advanced CDSS on their own or if they should “buy” proprietary CDSS products.

Stay tuned for the Part 2 of this blog post by Dr. Ujjwal Rao

Author

Dr. Ujjwal Rao

Dr. Ujjwal Rao is Senior Clinical Specialist in Integrated Decision Support Solutions, and is based in New Delhi, India. He provides strategic counsel to health providers on designing world-class clinical decision support systems with Elsevier’s comprehensive suite of current and evidence-based information solutions that can improve the quality and efficient delivery of healthcare.

An experienced emergency physician, executive, clinical informaticist and technology evangelist, Dr. Rao has a decade of experience serving in trust and corporate hospitals in various roles ranging from clinical administration, hospital operations to quality & accreditation. In his former positions, Dr. Rao led EHR implementations for large hospital groups and designed bespoke healthcare analytic solutions to raise profitability.

His passion to see transformation through technology led him to volunteer as a quality consultant with the United Nations. He also currently serves as an Assessor on the Panel of the Quality Council of India for the National Healthcare Accreditation Standards body, NABH.

Dr. Rao obtained his degree in Medicine and then specialized in Hospital and Health Systems Management, Medical Law and Ethics before completing his PhD in Quality and Medical Informatics.

How do we value your #startup?  Part 2 by Arpit Agarwal, @arpiit


How do we value your startup? — Part 2

In the previous post we talked about how VCs perceive valuation and how to broadly deal with it. It was aimed to dispel some misconceptions most first-time entrepreneurs may have about this very important aspect of our business. This posts builds on that and another and gives you actual numbers to play with. Before you go on, it maybe a good idea to take a look at the way I define stages of a startup.

Round sizes and dilution benchmarks in India

Now that you know the only two thing that matters to a VC is the stake she’s getting and the amount she’s investing, it is best that you play accordingly. If you’re seen as obsessed too much about valuation, you’re likely to be considered a hard-nosed founder. So be smart and play for ‘lower dilution’ instead. In case you argue that these are one and the same thing, think again 🙂
The table below presents a broad structure of startup funding in India today. This is what is considered ‘average’ in our industry. Please note that all these numbers are only representative in nature and deals frequently happen on both sides of these extremes. Also, like any well-functioning market, these prices represent only a momentary equilibrium and this changes over time:
© Blume Ventures, 2018 — Please don’t republish without permission. Write to aa+help@blume.vc
What are the Terms and Conditions?
  1. At any stage of investment, the investor is making a forward judgement on the exit outcome. Hence, it is common for investors to bake the exit scenario in the way you are being valued. For example, if a Series A investor, who’d typically expect a startup to exit at $500M, may value a startup which may exit at only $250M (in their minds) much more harshly than a startup which could easily build a $1B or more as an outcome.
  2. All the rules of the market —namely, demand and supply — still apply. It is not uncommon for serial/exited entrepreneurs to raise money at 5–10x valuation than first-time entrepreneurs.
  3. Valuation remains the last step. Hence, if you don’t pass any of the filters a VC has in their model, your willingness to price yourself very attractively doesn’t count.

How can you use this information?

First, it is always a great idea to know what’s the playbook on the other side. At the same time, smart people would know that such discrete classifications can’t be taken for granted. Second, whenever you have term-sheet on offer, you should know that valuation is largely formulaic for a VC, hence pay immense attention to the terms that come along with it. That’s the detail most first-time entrepreneurs struggle with!
Finally, as most experienced entrepreneurs will tell you,

forget valuation and focus on creating things of value.

If there’s value, valuation cannot be far behind. And creating anything of value is incredibly hard in any economy, doubly so in India. Once you are past your curiosity about valuation, you’d notice that life after raising VC money becomes much harder. In fact, the more you raise (by corollary, the higher your valuation) the more complicated it will get!

What’s more?

Hope these two posts satisfy a lot of your curiosities. As you can imagine, the art of valuing a startup is much deeper than what we covered here. Do post your responses and ask me if I skipped a step in my explanation.

The article was first published in the Arpit’s Medium Blog here,  and has been republished here with the Author’s permission.

Author
Arpit Agarwal

Arpit has been involved with promotion of startups and ecosystem since 2006 when he co-founded Headstart Network, today India’s largest early stage entrepreneurs’ network with over 20 city chapters.

Arpit has been with Blume for last four years as a Principal, responsible for scouting science-led and hardware businesses, apart from managing a portfolio of about 15 companies, which include companies in a variety of sectors ranging from used goods market to IOT to Healthcare to Enterprise services.

He’s based in Delhi, in an MBA from IIT Bombay and holds a BTech from NIT Trichy

How do we value your #startup?  Part 1 by Arpit Agarwal, @arpiit


This is the favorite topic of every single startup entrepreneur in early stages of their evolution. It also incites an academic curiosity in a large number of people who, like the 3 adorable dads in this video, have a highly misplaced notion about it. A big reason why this happens is because we don’t write about it so often in India and, perhaps, everyone understands this quite well in US or China.


Before I begin talking about it, I must put the usual disclaimers — this applies mostly to a tech startup in India, comes from our colored experience of valuing companies and often reflects the mood of the industry at this point of time. Each of these are important as we will see below. Also, please read my prequel post on stages of evolution of a startup to get the context and definitions right.

Valuation is in the eyes of the beholder

It won’t be a hyperbole if I say that startup valuation is more of an art than a science. There are only a handful of thumb-rules and rest everything is a highly subjective item. Another thing to understand about valuation is that while a general thumb rule is widely accepted, there are several well-known exceptions that prove the rule that this is a very subjective exercise.
In most cases, it will appear as if you have been valued less by your investors and the startup run by your friend was valued much higher. Please note that getting a round itself is relatively rare event. You can do your best to optimize on the available options, but once you are past it, look ahead and move on. Basically,

A high or low valuation is not simply a function of you, your business or the traction. Raising money is a highly subjective topic and by no means should a founder value her ‘true worth’ by it, because it is not.

Valuation is the final step

In my sales job, I learned a very key lesson that applies to many things in life: pricing is always the final step. Why? Because pricing is something that brings the deal to a Yes or a No situation. It is also a true test of how well the value has been explained to the buyer. Most institutional investors won’t even talk about valuation and deal structures unless they have gone through several rounds of discussions, both with you and internal. Our process mirrors a series of filters. There are easily tens, if not hundreds, of filters that we apply before we decide to roll out the term-sheet. And you should know that this is a Series of filters and it is rare that someone is able to bypass any of them. But that topic deserves a post of its own.

Demand-Supply rule

Let’s assume that you fortunately passed through these filters easily and there’s strong expressed intent of the investor to invest in your company. One dirty secret that you need to remember is — there’s no real science behind valuing a startup, in any economy, leave alone India where the availability of market data is quite shallow in itself. Coupled with the fact that technology is going to, sooner or later, disrupt existing markets (think book retailing) or create completely new markets (think local taxis), it is nearly impossible to accurately value early stage startups. Hence,

Valuation of a startup is a function of the demand it is generating in the investor market

As simple as that! Now, if you argue that at some point this valuation should catch up with the public market multiples, you are right. But that will happen at least 5–7 years after the first round of funding. A lot of technology, regulation and other macro factors can change in this period. Hence, experienced investors have come up with thumb-rules that they use to propose a deal.

A word on the VC business

Each institutional investor has a clearly thought-out strategy to deal with investments. This determines what kind of companies they like (think of filters, as above), the kind of exit value they seek and what is the minimum they want to make as a return per investment.
For example, Blume Ventures loves all things tech — both B2B and B2C. Conversely, we have tried and failed in building consumer brands. Hence, we don’t like to invest into your apparel brand or restaurant chain or even a niche e-commerce business. If we choose to invest into a company, we expect it to deliver at least a $100M exit value and we want to give back our LPs at least $10M at your exit. This means we need to be holding at least 10% at exit. Given 3–4 rounds of dilution after our first check in, it is possible to hold 10% at exit only if we start closer to 18–20%. Further, as a constraint on our fund size ($60M) and the number of companies we want to invest in (40), our ticket size per first check is about $500k-1M (₹3.5–7Cr) or thereabout. Our ideal scenario is that we start with approximately 20% stake in the first round. Some of our recent term-sheet reflect this strategy exactly as I described.
But, did you notice that we didn’t even think of a valuation so far? That’s because

Valuation is a derived amount in VC business

We don’t obsess about valuation so long as our stake target is met and the check size doesn’t exceed our comfort level.

Stay Tuned

This post must have given you some food for thought on how to deal with investors and funding rounds. In Part 2, we will talk about the benchmark numbers, round sizes and, most importantly, how can you use all this knowledge to your best advantage.

The article was first published in the Arpit’s Medium Blog here,  and has been republished here with the Author’s permission.

Author
Arpit Agarwal

Arpit has been involved with promotion of startups and ecosystem since 2006 when he co-founded Headstart Network, today India’s largest early stage entrepreneurs’ network with over 20 city chapters.

Arpit has been with Blume for last four years as a Principal, responsible for scouting science-led and hardware businesses, apart from managing a portfolio of about 15 companies, which include companies in a variety of sectors ranging from used goods market to IOT to Healthcare to Enterprise services.

He’s based in Delhi, in an MBA from IIT Bombay and holds a BTech from NIT Trichy