SocmedicaDecision Support System for clinical practice created

on the basis of the United Medical Knowledge Base

1. Problem

Medical errors and unpredicted complications on various stages of diagnostics and treatment

As a consequence:- high mortality rate- unpredicted complications- enormous costs

1. ProblemAnatomy of medical errors

Due to medical errors:Due to medical errors:170 000170 000 patients annually get disabled 50 000 50 000 deaths occurs annually www. лига защиты пациентов

440 000440 000 deaths occurs annually due to medical errorsJournal of Patient Safety, Forbes

Before a medical error goes public, it pass through a multilevel “filter”:

1. A large number of errors remains on physician’s conscience; a half of medical specialists does not even realize their fault.

2. Errors are partly covered up by solidarity medical community.

3. Next, management of the institution cares about its statistics.

Only numbers that pass all three filters are published.

1. ProblemCosts of medical errors

$ 7,3 B$ 7,3 B annually spent on medical errors www.cpmhealthgrades.com

An average general in-patient institutions spent annually ≈ ≈ $1,5 M$1,5 M on medical errors

1. Problemof health insurance company

Lack of supervision of treatmentCompliance of diagnostic and treatment procedures to the applied standards as well as reasonableness of the applied standards to a particular patient is a complex routine task. It requires a staff of experts involving different medical specialists. The result is fraud and abuse of services provided by insurance companies.

Prediction of possible „costly” diseases in a client.

Decision making after insurable event requires a big staff of highly qualified experts.

2. Demand / market

A substitute is Electronic Health Records systems (EHR systems).

Main consumers– b2b – medical institutions, insurance companies and manufacturers

of EHR systems;– b2c – physicians, patients and internet users, who care about their

health;– b2g – government agencies that participate in the computerization of

healthcare

Volume of the Russian market of EHR systems is 11.5 billion RUB per year.

Its growth rate is 9 % per year.

2. Demand / marketGlobal market

EHR – market of Electronic Heath Records systems (Medical IT systems)CDSS – target segment (Clinical Decision Support Systems)• Market size for 2014 - $3,74B;• CAGR - 25%.According to Marketsandmarkets.

Increased demand for analytical IT decisions that allow to reduce the probability of medical errors will drive the growth of global market decision support systems for clinical practice. They will reduce costs of clinics and improve quality of healthcare.

2. Demand / marketCompetitive comparison of the existing expert systems

Expert systems Targeted audience

Personification of patients Forecasting Diagnostics Decision

support Number of diseases Input data Self-learning

Socmedica* pat, doc, clinic Y Y Y Y all any YIBM Watson* pat, doc, clinic ? Y Y Y oncology, urology any YЭксперт doc N N N Y 3 sympt, lab NPxdes doc N N Y N 1 (pneumoconiosis) x-ray NEMERGE doc, clinic N N Y Y chest pain sympt, lab NCaDet doc, clinic N Y Y Y oncology sympt, epid NApache III doc, clinic N Y N Y severity of patient's

condition sympt, lab NDXplain doc, clinic N N Y Y 2400 sympt, lab NGermwatcher doc, clinic N N Y Y hospital infections lab NPEIRS doc, clinic N N Y N laboratory interpretation lab NPuff doc, clinic N N Y N pulmonary pathology sympt, lab NSETH doc, clinic N Y Y Y clinical pharmacology sympt, lab Neasydiagnosis pat, doc N N да да ? (main groups of diseases) sympt Nnhsdirect pat N N N N

? (main groups of diseases) sympt N

webmd pat N N да N? (main groups of

diseases) sympt Nsymcat pat N N да N 800 sympt, epid N

The existing expert systems are usually a local solution to a narrow range of issues.The only example of a complete system of decision support and potential competitor is IBM Watson super computer, which now undergoes clinical testing.* Main advantages of Socmedica over IBM Watson:1. Along with question-answering communication method with the system, included into IBM Watson model, Socmedica uses a principle of background monitoring of clinical material of a patient. We believe that most physicians do not realize that they commit errors; therefore, they will not make any requests to the system. Other physicians are too busy to make requests in timely manner. Our approach minimizes the human factor that leads to errors.2. In Socmedica system, a search for answer is similar to thinking pattern of a physician; semantics speak in terms of medical ontologies.

3. SolutionDecision support system for clinical practice

EMR Virtual image of a patient

Decision support system

1. Anonymized electronic medical record (EMR) is uploaded to the cloud2. Any EMR format can be analyzed3. The system creates a virtual image of the patient4. The virtual image is constantly adjusted and optimized in the background

5. The system processes any inquiries up to the moment of discharge of the patient 6. Physician receives recommendations at the workplace

3. SolutionExample: Recommendations of the system displayed on physician’s PC

Риск развития тромбоэмболии

легочной артерии

73%Прогнозирование рисков возникновения осложнений

Ранняя диагностика госпитальных осложнений

Рекомендации системы по профилактике, дифференциальной диагностике и лечению осложнений

Мониторинг за лечебным процессом и состоянием пациента

4. Basic technology

UMKB

M

Model of presentation of medical knowledge

System of modeling of knowledge

United Medical Knowledge Base (UMKB)

Algorithms of predictive analytics and decision support

4. Basic technologyTechnology of medical knowledge repre

М

We developed a model of medical knowledge representation that combines and structures the information offered by various areas of medicine from clinical practice to molecular biology and genetics.

13

4. Basic technologyTechnology of medical knowledge representation

Constructor of ontologies

Crowd-sourcing

system of knowledge modeling

Computer-ized analysis of medical

texts

Integration of knowledgeFormation of

evidence level

Real-time analysis of EHRs with

data extraction

14

4. Basic technologyComputerized analysis of medical texts

ABBYY Morphological

and Lexical Analyzer

4. Basic technologyResult of text analysis

4. Basic technologyExample: Modeling of the pathogenesis of myocardial infarction

Intellectual propertyObtained patents and copyrights

• Modeling system for medical knowledge base – Socmedica (State Registration Certificate of a Computer Program no. 2014618583)

• Unified classifier of medical terms “Socmedica-MT” (State Registration Certificate of a Database no. 015620304)

• Patent “Method of determination of drug interactions and contraindications for drugs with the use of a structured knowledge base”. Application no. 2015111641 on the 31st of March, 2015.

• Patent “Method of automatic selection of drugs”. Application no. 2015111641 on the 31st of March, 2015.

• Graphical user interface of a decision support system for drugs prescription. Applications no. 2015501457 and no. 2015501457.

• Trademark Соцмедика/Socmedica (Certificate of trademark no. 528331)

• Corporate identity (Trademark Certificate no. 494814)

• Algorithm of predictive analytics and differential diagnosis – (patent applications are prepared)

Experimental prototype of the Clinical Decision Support System for drug prescription is already available at

http://www.socmedica.com/page/pharm_expert

5. Business modelCommercialization plan

1. Mobile version of the product. On the initial stage it will be distributed among physicians and patients free of charge in order to scale of the project quickly. Subsequently, this direction can be monetized through advertising or subscription fees. 2. Installing the product in already existing electronic health records systems (EHR systems) in healthcare institutions. System can be introduced into the structure of any EHR system. After that, it analyzes medical e-records in the background mode and gives conclusions with practical recommendations. Physicians regularly see and consider these recommendations in their workplace. The cost of installation of the expert system is averagely $200 per year per workplace. The price will vary depending on the number of workplaces. Additional source of income will be license renewal and system management services. Planned sails volume is $1.2 million in 2016, and $17.5 million in 2020 (after 4 years). 3. Analysis of anonymized medical e-records in cloud. Healthcare institutions upload anonymized EHRs of patients into cloud to analyze them with expert system. The system creates a virtual image of a patient and processes any requests up to the discharge of the patient from the hospital. Decision support for one patient image will cost $10—$15 depending on the specialization of a unit, which will be covered by healthcare institutions. Planned volume of sails is $0.5 million for 2016, and $1.9 million in 2020 (after 4 years). 4. Sell of product licenses to manufacturers and/or suppliers of EHR systems. After purchase of a license, manufacturers and/or suppliers of EHR systems will be able to implement into clinics their own products with already existing clinical decision support system. Today we actively negotiate with such potential partners. Manufacturers and/or suppliers of EHR systems are interested in the integration of a clinical decision support system to improve quality and competitiveness of their EHR. We have already made a preliminary arrangement with CompuGroupMedical (CGM) about integration of the developed product into their system CGM CLININET. CGM is one of the world-leading companies in the eHealth area. It delivers EHR systems in 35 countries to more than 385 000 clients. It is a good chance for us to enter the international market.

5. Business model

131 000EMRs monthly

131 000EMRs monthly Decision

support 600 RUB

Decision support 600 RUB

Virtual image of a patient

Expert decision support systemExpert decision support system

in-patient units of health care facilities (potential clients) -

661

in-patient units of health care facilities (potential clients) -

661

Insurance company (compulsory and voluntary

medical insurance)

Insurance company (compulsory and voluntary

medical insurance)

Report on quality of medical care 300

RUB

Report on quality of medical care 300

RUB

Certificate of insurance

Certificate of insurance

Why will health care institutions buy our product?

3. The use of our product:4. 1. decreases the number of medical errors and

unpredicted complications5. 2. cuts costs and gives additional income.

3. provides individual approach to every patient4. improves quality of medical care5. reduces mortality6. improves competitive position of a facility7. attracts inflow of patients

+

Example of surgical hospital unit with 650 beds, which spends about 20 million RUB monthly on medical errors

Additional monthly income due to the increased patient inflow.

6. Key team members

G. A. Blejyants. CEO. Cardiovascular surgeon, PhD in medicine.More than 14 years of clinical practice.Experience in the development of medical classifiers.Author of the model of medical knowledge representation.

N. A. Tumanov. Executive director. Psychiatrist, PhD in medicine.More than 14 years of clinical practice.Experience in modeling of medical knowledge.Experience in the creation of algorithms that operate similar to thinking pattern of a physician.

Yu. A. Isakova. Head of the project in pharmacology and pharmacy. Pharmacist, leading researcher of the Dpt. of Clinical Pharmacology, Research Clinical Center “RZD”.Member of Russian Society for Evidence Based Medicine, Russian & International Society for Pharmacoeconomics and Outcomes Research (RSPOR & ISPOR).

A. V. Panosyan. IT directorSoftware developerExperience in the creation of system managing artificial neural network.Author of the modeling system of medical knowledge.

M. Guseynov. Chief programmer.Software developer.Experience in creating self-learning database.Experience in creating systems managing artificial neural network.

M. G. Abgaryan. Director for external relations. PhD in engineering. Experience in implementing systems of storage, processing and visualization of medical images in Russian healthcare institutions. Experience in organizing development and production of professional graphic DICOM station

A. V. Lapuk. Mentor, specialist with international experience in the field of medical research. Molecular biologist. Full member of the American Association of Cancer Research, New York Academy ofSciences. Her valuable research experience helps the team to search for alternative niches for application UMKB. Scientist at Vancouver Research Institute of HealthCare (British Columbia, Canada).

R. S. Melkonyan. Director of Medical department. MD.Cardiovascular surgeon, therapist. 14 years of clinical practice. Has been studying and creating algorithms of expert system for “General practitioner” system for the last 5 years.

M. A. Sarkisian. Director of the Division on scientific collaboration and interacademic relations. Professor at Yevdokimov Moscow State University of Medicine and Dentistry. 18 years of experience in clinical practice. His main task is to involve academic and scientific community (from students to senior specialists) in the process of UMKB modeling.

G. N. Mdinaradze. Specialist in insurance medicine. PhD in stomatology.16 years of clinical practice. Since 2011 he holds position of CEO of the OOO “Rosneft Zdorovye” and Deputy Head of Department of Social development of the OAO “NK Rosneft” where he developed and implemented programs of insurance and medical support for employees of the “Rosneft”.

S. V. Vartanyan. Deputy director of external relations. Employed in the ZAO “Socmedica” in the Division of cooperation with centers of medical science and medical facilities to fill UMKB. Supervises commercial issues.

6. Key team members

7. Stages of project development and investments neededand required volume of investments

$1.75M

$1.75M

A technology for modeling of medical knowledge has been developedPartnership relations with research centers have been achievedUnited medical knowledge base has been createdWorking prototype is ready: a decision support system for drug therapy

Decision support system for clinical practice

Stage 2:System of risk prediction for clinical complicationsAlgorithms of risk prediction, which will be used in the expert system…

Stage 3:Expert diagnostic system „Electronic therapist”Diagnostic algorithms, which will be used in the expert system…

Stage 4:Expert system of personal medical user support „Personal doctor”Development of a module for the creation of individual image of patient…

1 stage completed

Final product

8. Financial forecast

On the current stage active negotiations with prospective purchasers are carried. Contracts have been signed; the first sell is expected within 4 months. 14 month after required investments, our company will become self-financed. Two years after the beginning of financing, net profit should be about $0.2 million. We predict possibility of withdrawal of an investor in the beginning of the 3rd year by selling its share to a strategic investor. As such strategic investors, we regard large companies, leaders of EHR market.

Gevorg BlejyantsТел: +7 (926) 991-10-41E-mail: blejyants@socmedica.com

www.socmedica.com

Nikolai TumanovТел: +7 (916) 625-90-40E-mail: tumanov@socmedica.com

Socmedica