GLOSSARY OF TERMS

Glossary of terms and acronyms for #Blockchain and #Cryptocurrency

/ Uncategorized / 13 minutes of reading

Blockchain: The foundational technology behind the blockchain and cryptocurrency sector. It is a virtual, immutable (unchangeable), distributed store of data stored on servers around the world. This is a new way of distributing both trust and data. It is an alternative to traditional systems where a central organization holds all the data.

Think of it as a chain of blocks of data, verified by consensus by any computer that chooses to participate. Each block of data containing anything from who has sent cryptocurrency to others to who owns what plot of land in a land registry.
Blockchain is a distributed ledger.
Block: A package of data containing multiple transactions over a given period of time.
Chain: The cryptographic link that keeps blocks together using a ‘hash’ function.
Distributed ledger: This is an analogy often made about blockchains. Instead of a centralized bank ledger, blockchains offer the promise of distributing balances throughout a network of computer servers.
You aren’t going to a single bank to store where you send your value — instead, you are going to a decentralized network of peers.
Distributed ledgers aren’t a new concept: the island of Yap used individual tables as early as 500 AD. They yelled at one another whenever they made a new transaction. Blockchains and cryptocurrencies offer the global, virtual network equivalent of that system.
Cryptocurrency: A token or currency built on top of blockchain technology. This token helps capture and distribute value from users of the blockchain. You can think of Bitcoin as the first application and cryptocurrency stemming from the blockchain. Cryptocurrencies are a subset of what are known as cryptoassets. 
Tokens: The means of exchange to give value to a transaction; typically a native cryptocurrency. Some non-currency blockchain architectures can be tokenless.
Cryptocurrency Exchange: Cryptocurrency exchanges are websites or services that let you exchange digital cryptoassets and cryptocurrencies between one another or exchange fiat currencies such as the US dollar for cryptoassets. Two of the most prominent examples of these exchanges are Coinbase and Binance.
Public/Private Keys: Keys are your way to access crypto balances and to send and receive value or data in cryptocurrency. Your public key is like your email address. It’s what allows other people to send you funds. You can share your public key with the general public.
Services like Etherscan can scan account balances and transactions associated with a public key.
Private keys are the password to your email account. Anybody who holds the private key to a wallet can access and control it and spend any tokens within it. It’s a unique string of data that represents proof of identification within the blockchain, including the right to access and own that participant’s wallet within a cryptocurrency. It must be kept secret: it is effectively a personal password
A unique string of data that identifies a participant within the blockchain. It can be shared publicly.
If you lose your private key or forget it, you’ll lose control of all the crypto assets tied to that private key/public key combination.
Cryptocurrency Wallets: Cryptocurrency wallets are ways of storing your private and public keys to your cryptoassets. A wallet is a safe you can access to then get your keys.
Wallets allow for easier access and backups if you don’t remember your private key with techniques such as the mnemonic seed phrase, a series of 25 random words you have to input to get access to your private key.
There are software wallets and hardware wallets: software wallets store your keys online, while hardware wallets use a physical device such as the Trezor to protect your private key.
MultiSig: MultiSig is a permissions system for crypto wallets. The majority of cryptocurrency wallets are single-signature. This means you only need one person’s private key to control the balance within it.
MultiSig means you need more than one private key to spend funds. This allows you to set up an M-of-M scheme. As an example, you might need 5 out of 9 signers to approve of a transaction for it to go through. This is useful for corporate wallets, where many owners and employees have to approve before a transaction is sent.
Platforms like BitGo and Xapo provide MultiSig wallets for their users.
Proof-of-work: A system where blocks of transaction data on the blockchain are mined and validated by specialized computers who earn a reward for solving specific math equations. Repeatedly running a hash function, the mechanism by which data miners win the right to add blocks to a bitcoin-style blockchain.
Mining: A practice in proof-of-work systems where computers are dedicated to solving math problems in order to claim the right to mine a block of data and to get an amount of cryptocurrency.
How it works in some more detail: the cryptographic mining piece involves solving cryptographic puzzles. A computer needs to find a nonce to combine with unverified transactions to output a verified string.
Data Mining:  The process of solving cryptographic problems using computer hardware to add newly hashed blocks to a public blockchain such as bitcoin. In fulfilling this function, successful data miners keep the blockchain actively recording transactions and, as an incentive, are awarded newly minted bitcoins for their trouble.
Mining pool: A mining pool aggregates computing resources dedicated to mining cryptocurrencies and allocates any of the mined blocks proportionally. In practice, mining cryptocurrencies has some randomness to it, so mining pools serve an essential purpose in keeping volatility down for individual miners.
Proof-of-stake: Proof-of-stake pushes people who own a selection of a blockchain’s tokens to make decisions on validating the chain. In practice, it’s a much less energy-intensive practice than mining.
Resource to read: Proof-of-stake – Wikipedia
Node: Any computing server around the world can run as a cryptocurrency node, which can store a copy of the blockchain and serve to verify transactions.
Hash The result of applying an algorithmic function to data in order to convert them into a random string of numbers and letters. This acts as a digital fingerprint of that data, allowing it to be locked in place within the blockchain.
Hash Rate: A measure of the computing power dedicated to any blockchain by the miners validating transactions and blocks. The higher the hash rate, the more active the chain is and the more appealing it is to miners. It then becomes harder to attack the chain, and infiltrate it with false transactions (known as a 51% attack).  
Decentralization: A measure of how much authority is held by a central holder. You can argue that blockchains are naturally more decentralized than other methods of distributing data because there is (at least in public chains) no gatekeeper on who can join: as long as you have the computing power, you can participate in the blockchain.
Instead of all of your data residing in one central provider (ex: Equifax), it now sits and is processed and verified by a global network of computers.
Decentralization is an ideal of the blockchain community. However, it has not been perfectly achieved.
For example, the mining pools that mine most of Bitcoin are mostly based in China: a consortium of these mining pools might decide to do what is called a 51% attack. They would use their assembled computing power to change the rules of the blockchain and facilitate conditions such as “double spend”: the ability to infinitely spend the same block of cryptocurrencies, essentially creating wealth out of nothing. The control of mining resources is very centralized.
Public vs. Private Chains: There are blockchains open entirely to the public (anybody can participate) such as Bitcoin and Ethereum. There are also private blockchains that have gatekeepers who determine who can join.
Private blockchainA closely controlled network operated by consortia in which the data is confidential and is accessed only by trusted members. Private blockchains do not require a token.
Interoperability: Blockchains and cryptocurrencies are often isolated with one another, and need to be exchanged in order to be used.  
Blockchains like Aion are looking to solve the interoperability piece by making different blockchains and cryptocurrencies interoperable, or compatible with one another: imagine, for example, a world where you can trade Bitcoin and Ethereum seamlessly (without exchanges) and use them interchangeably.
Atomic Swaps: Atomic swaps involve cryptocurrencies that are tradeable with one another without needing an exchange in the middle. Typically, they have to follow the same encryption standard and have a payment channel protocol such as Lightning Network. With what’s called a hash-time locked smart contract, two individuals can trustlessly trade cryptocurrency pairs with one another: solving the interoperability piece.  
Hash functions/tables: A more technical and precise description of the underlying technical foundation of how data is shared and stored on a blockchain. Hash tables are a mainstay of computer science.
Bitcoin: Bitcoin was created by Satoshi Nakamoto in 2008 as the first application of the blockchain and as the first cryptocurrency. It is still the dominant cryptocurrency now.
Fork: There are soft forks, where a cryptocurrency maintains its value and its rules are simply rolled forward and changed in a reversible manner, usually with the assent of the majority of the community.
Hard forks are when a blockchain fails to reach consensus and has to do a hard reset and splits off into two chains. One chain adopts one set of rules and another continues the original set of rules. This is non-reversible. A hard fork is how Bitcoin and Bitcoin Cash split.
SegWit: Segmented Witness (or SegWit) is a soft fork that happened with the Bitcoin blockchain. It solved congestion on the network by increasing the blockchain’s block size limit and splitting blocks of data in two. It separated out the unlocking signature with the scripts that send and receive data with the transactional data.
This allows the network to process more transactions per second. Users don’t have to wait as long for bitcoin transactions.
Resource to read: Segregated Witness, Part 1  
Lightning Network: Lightning Network is an off-chain solution that can settle transactions without having to use the underlying blockchain. It opens up bidirectional payment channels between different individuals, allowing Bitcoin to process many more transactions per second.
Payment channels have pre-deposited amounts of crypto placed into them. They allow individuals with channels open between them to transact seamlessly without using the blockchain. Once you get a final balance, it is validated into the blockchain.
This allows for many more payments to be done per second. It also means there is some centralization between large payers.
Resource to read: Lightning Network
Schnorr: A large Bitcoin update brewing as of the time of drafting for this article, Schnorr proposes to give users a new way to generate the private and public keys critical to cryptocurrencies. It replaces the Elliptic Curve technique currently used to generate keys with the Schnorr technique.
This update increases both privacy and security by grouping together MultiSig and regular transactions in the same category, allowing the blockchain process to more transactions and hiding whether or not a transaction is MultiSig or not.
Ethereum: The blockchain behind the second largest cryptocurrency. Ethereum differentiates itself from Bitcoin by allowing programmers to build on top of the blockchain with a Turing-complete programming language. This allows programmers to build distributed applications.
While Bitcoin can be seen as one application (transfer of value) on the distributed web just like email, Ethereum is a network that allows for many different applications to come to the fore.  
The cryptocurrency associated with the Ethereum blockchain is known as Ether.
Decentralized Apps (DApps): A decentralized application is a specific type of app that serves a specific purpose within a blockchain network. It must be open-source, autonomous, and it must make changes to the underlying software via consensus from its users. It must store all its data on a public blockchain, which is auditable by the public, and it must generate tokens and be accessible via those same tokens.
DApps seem like regular web applications. Client-side, the same mechanism is in play, but server-side (or the back-end), data and control are distributed among a network of P2P (peer-to-peer) nodes and smart contracts rather than a centralized set of servers and server code.
Resource to read: DApps – Flipside Crypto
Smart Contracts:  Smart contracts refer to code that is placed on a blockchain and is then executed on it. The code can be audited by the public. Smart contracts are often regarded as a compliment or a replacement to traditional legal contracts.
A smart contract might algorithmically implement escrow payments without having the need to create a binding legal contract to hold parties accountable.
However, the term is often seen as overly broad as it can mean any block of code placed on the blockchain.
Gas: Gas is used as a transactional cost in the Ethereum blockchain. When you use Ether to access distributed applications, you have to spend a portion of gas associated with it. Gas is correlated with how much computational work your request takes. This ensures that transactional costs are rightly set for the amount of work the system needs to do.
Gas is a way to ensure that nobody tries to attack the Ethereum network by filling it with invalid requests.
Solidity: Solidity is currently the most popular programming language to write smart contracts on the Ethereum blockchain, based around EMCAScript (the basis of JavaScript).
DAO: DAOs or decentralized autonomous organizations are a collective grouping in which smart contracts make choices. The entire organization is run on the blockchain. Shareholders buy tokens that give them the right to vote on future decisions.
Resource to read: DAO – Flipside Crypto
Casper:  Casper is an implementation of the Ethereum blockchain that promises to process more transactions per second. Ethereum used to be able to process 20 transactions a second. Bitcoin could only process 4. Visa and Mastercard can process about 20000 transactions a second. Casper is an in-between step for the Ethereum blockchain to change over from proof-of-work to proof-of-stake. It implements sharding (dividing the main Ethereum chain into smaller subcomponent chains) to provide parallel processing and increased throughput.
ERC20: A set of standards based on the Ethereum blockchain. ERC20 allows anybody to create a token built on top of Ethereum’s blockchain. It is the basis of the initial coin offering craze and the advent of new “altcoins”.
Altcoins: Altcoins are tokens, cryptocurrencies and cryptoassets outside of Bitcoin and Ethereum. Coinmarketcap gives you a good view of how many there are!
Stablecoins: Stablecoins are cryptoassets pegged to a certain value or asset — for example, you have stablecoins that trade 1:1 with the US dollar. These are collateralized or not with other cryptoassets.
Initial Coin Offering: Another way to originate tokens for a blockchain. An ICO involves a marketing process, private sale, then a public sale of a newly-listed token, which then aims to be listed on as many cryptocurrency exchanges as possible. Note that there is no standard way of conducting initial coin offerings.
Hyperledger An umbrella project set up by the Linux Foundation comprising various tools and systems for building open-source blockchains.
Oracle: A bridge from a blockchain to an external data source that allows a smart contract to complete its business by referencing timely real-world information. An oracle might allow a smart contract to access consumer energy usage, live train timetables, election results, and so on.
Peer-to-peer (P2P)The direct sharing of data between nodes on a network, as opposed to via a central server. 
Permissioned ledgerA large, distributed network using a native token, with access restricted to those with specific roles.
Proof of stake The mechanism by which participants earn the right to add new blocks and so earn new tokens, based on how much of that currency they already hold.
Public blockchainA large distributed network using a native token (such as bitcoin), open to everyone to participate and maintain.

References: 

[1]: A definitive glossary of blockchain and cryptocurrency terms: 
https://thenextweb.com/contributors/2018/08/21/a-definitive-glossary-of-blockchain-and-cryptocurrency-terms/

[2]: Flipside Crypto’s guide to blockchain and cryptocurrencies:  https://flipsidecrypto.com/wealthadvisor-ebook/
Team @HCITExperts [Updated: 1st Sep 2018]
Author
Team HCITExperts

Your partner in Digital Health Transformation using innovative and insightful ideas

Glossary of Terms for Healthcare Data Analytics

/ Uncategorized / 8 minutes of reading

BALANCED SCORECARD:
A framework developed by Robert Kaplan and David Norton that suggests four perspectives of performance measurement to provide a comprehensive view of an organisation. These are service user perspective, internal management perspective, continuous improvement perspective and financial perspective.


BENCHMARK:

A point of reference or standard by which something can be measured

BENCHMARKING:

The process of comparing the cost, cycle time, productivity, or quality of a specific process or method to another that is widely considered to be an industry standard or best practice.

CASEMIX:

Casemix is an internationally recognised system of measuring clinical activity incorporating the age, gender and health status of the population served by an organisation with a view to objective determination of hospital reimbursement.

DATA:

Data are numbers, symbols, words, images, graphics that have yet to be organised or analysed

DATA DICTIONARY:

A descriptive list of names (also called representations or displays), definitions, and attributes of data elements to be collected in an information system or database.

DATA ELEMENT:

A unit of data for which the definition, identification, representation, and permissible values are

DOMAINS OF QUALITY:

Are those definable, preferably measurable and actionable, attributes of the system that are related to its functioning to maintain, restore or improve health

DESCRIPTIVE ANALYTICS

As per gartner, Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?” (or What is happening?), characterized by traditional business intelligence (BI) and visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives.

PREDICTIVE ANALYTICS

As per gartner, Predictive analytics describes any approach to data mining with four attributes:

1. An emphasis on prediction (rather than description, classification or clustering)
2. Rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining)
3. An emphasis on the business relevance of the resulting insights (no ivory tower analyses)

4. (increasingly) An emphasis on ease of use, thus making the tools accessible to business users.

PRESCRIPTIVE ANALYTICS

As per Gartner, Prescriptive Analytics is a form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make _______ happen?”, and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

HEALTH INFORMATION:

Health Information is defined as information, recorded in any form or medium, which is created or communicated by an organisation or individual relating to the past, present or future, physical or mental health or social care of an individual or cohort. It also includes information relating to the management of the health and social care system

KPI SELECTION CRITERIA

KPIs should be chosen based on the judgement and consensus of experts and potential users. The List of characteristics and related questions which can be used to assist in the identification of KPIs. Adapted from criteria developed by the World Health Organization (WHO). 


Validity 
Does the KPI measure what it is supposed to measure? A valid KPI measures what it is supposed to measure and captures an important aspect of quality that can be influenced by the healthcare facility or system. Ideally KPIs selected should have links to processes and outcomes through scientific evidence. Measures that have been selected using scientific evidence possess high content validity and measures selected through consensus and guidelines will have high face validity. Content validity refers to whether the KPI captures important aspects of the quality of care provided. Face validity can be determined by the KPI making sense logically and clinically or from previous usage.

Reliability 
Does the KPI provide a consistent measure? The KPI should provide a consistent measure in the same population and settings irrespective of who performs the measurement. Reliability is similar to reproducibility to the extent that if the measure is repeated you should get the same result. Any variations in the result of the KPI should reflect actual changes in the process or outcome. Reliability can be influenced by training, the KPI definition and the precision of the data collection methods. Inter-rater reliability compares differences between evaluators performing the same measurement. Internal consistency examines the relationship between sub-indicators of the same overall measurement, and, if reliable, there should be correlation of the results. Test-retest reliability compares the difference between results when the same evaluator performs the measurement at different times. 

Explicit evidence base
Is the KPI supported by scientific evidence or the consensus of
experts? KPIs should be based on scientific evidence, the consensus of expert opinions among health professionals or on clinical guidelines. The preferred method of choosing KPIs is through evaluating scientific evidence in support of each KPI and rating the strength of that evidence. One example of a rating system is to give the highest rating to evidence (“A” evidence) from meta-analysis of randomised controlled trials and give a lesser rating (“B” evidence) to evidence for controlled studies without randomisation and a further lower rating (“C” evidence) to data from epidemiological studies. 
In healthcare, there may only be limited scientific evidence to support a KPI and it becomes Guidance on developing Key Performance Indicators and Minimum Data Sets to Monitor Healthcare Quality Health Information and Quality Authority 33 necessary to avail of expert opinion. There are a number of methods by which a KPI can be developed through facilitating group consensus from a panel of experts, such as the Delphi technique, the RAND appropriateness method and from clinical guidelines. Appendix 2 gives a brief description of each method and Appendix 3 provides an example of a Delphi assessment instrument. The expert panel can exist independently of the advisory group and are used as a point of reference for the KPI development process
Acceptability 
Are the KPIs acceptable? The data collected should be acceptable to those being assessed and to those carrying out the assessment. 
Feasibility 
Is it possible to collect the required data and is it worth the resources? There should be a feasibility analysis carried out to determine what data are currently collected and the resources required to collect any additional required data. The feasibility analysis should determine what data sources are currently available and if they are relevant to the needs of the current project. This will include determining if there are existing KPIs or benchmarking processes based on these data sources.

The reporting burden of collecting the data contained in the KPI should not outweigh the value of the information obtained. Preferably, data should be integrated into service delivery, and, where additional data are required that are not currently part of service delivery, there should be cost benefit analysis to determine if it is cost-effective to collect.

The feasibility analysis should also include what means are used to collect data and the limitations of the systems used for collection. It should also outline the reporting arrangements, including reporting arrangements for existing data collection and frequency of data collection and analyses.

Sensitivity 
Are small changes reflected in the results? Changes in the component of care being measured should be captured by the measurement process and reflected in the results. The performance indicator should be capable of detecting changes in the quality of care and these changes must be reflected in the resulting values. 

Specificity 
Does the KPI actually capture changes that occur in the service for
which the measure is intended? Only changes in the area being measured are reflected in the measurement results

Relevance 
What useful decisions can be made from the KPI? The results of the measurement should be of use in planning and the subsequent delivery of healthcare and contribute to performance improvement

Balance 
Do we have a set of KPIs that measure different aspects of the service? The final suite of indicators should measure different aspects of the service in order to provide a comprehensive picture of performance, including user perspective

Tested 

Have national and international KPIs been considered? There should be due consideration given to indicators that have been tried and tested in the national and international arena rather than developing new indicators for the same purpose.

Safe 
Will an undue focus on the KPI lead to potential adverse effects on other aspects of quality and safety? The indicator should not lead to an undue focus on the aspect of care being measured that may in turn lead to a compromise in the quality and safety of other aspects of the service.
Avoid duplication 
Has consideration been given to other projects or initiatives? Prior to developing the indicator due consideration should be given to other projects or initiatives to ensure that there will not be a duplication of data collection.

Timeliness
Is the information available within an acceptable period of time to inform decision-makers? The data should be available within a time period that enables decision-makers utilise the data to inform their decision-making process. If the data is required for operational purposes, then it will be required within a shorter timeframe than data used for long term strategic purposes. 

METADATA:

Data that defines and describes other data

MINIMUM DATA SET:

The minimum set of data elements that are required to be collected for a specific purpose

NUMERATOR:

The specifications that define the subset of data items in the denominator that meet the indicator criteria.

KEY PERFORMANCE INDICATORS:

Performance Indicators are specific and measurable elements of practice that can be used to assess quality of care. Indicators are quantitative measures of structures, processes or outcomes that may be correlated with the quality of care delivered by the healthcare system. 

PROCESS INDICATORS:

Performance indicators that monitor the activities carried out in the assessment/diagnosis and treatment of service users.

OUTCOME INDICATORS:

Performance indicators that monitor the desired states resulting from care processes, which may include reduction in morbidity and mortality, and improvement in the quality of life.

RELIABILITY:

Reliability is the consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same condition with the same subjects.

STRUCTURE INDICATORS:

Performance indicators that monitor the attributes of the health system that contribute to its ability to meet the healthcare needs of the population.

The Delphi Technique:

The Delphi technique is a facilitated structured process whereby a panel of experts complete questionnaires (see Appendix 3 for example) remotely and, through feedback and scoring over a number of rounds where some KPIs are discarded, a consensus is achieved on a final set of KPIs. The panel need not ever meet face-to-face and each individual’s feedback is provided anonymously to the panel, which eliminates the possibility of undue influence by dominant personalities within the panel.

The RAND appropriateness method:

The RAND appropriateness method combines scientific evidence with expert
opinion by facilitating experts to rate, discuss and re-rate KPIs. Unlike the Delphi technique the expert panel meet face-to-face to discuss possible KPIs and are given a copy of the scientific literature in support of the KPIs so that they can ground their opinion on evidence-based literature

VALIDITY:

Validity of indicators refers to whether performance indicators are measuring what they are supposed to measure. e are constantly looking for Healthcare Informatics & Digital Health Experts to share their experiences by writing articles on Technology benefiting in the delivery of Healthcare Services.

And there you go, its fairly simple and we look forward to you sharing your experiences with our community of readers. We appreciate you considering sharing your knowledge via The HCITExpert Blog
Author
Team HCITExperts

Your partner in Digital Health Transformation using innovative and insightful ideas

Glossary of Healthcare & HealthIT Terms and Acronyms

/ Uncategorized / 14 minutes of reading


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

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

ADT – Admissions, Discharge & Transfer

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

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

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

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

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

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

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

Bio-sensing wearables

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 EMPI an enterprise master patient index

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Online patient communities

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

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

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

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

Personal health records (PHRs)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Telemedicine

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

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

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

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

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

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

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

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

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

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

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

LMIS – laboratory information management system

RIS – Radiology Information System

PACS – Picture Archival and Communications System 

DICOM – Digital Imaging and Communications in Medicine


References

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

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

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

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

Glossary of Terms & Acronyms for Artificial Intelligence and Machine Learning

/ AI/ML/DL / 7 minutes of reading

AI & Machine Learning Terms

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References


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

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

Scroll to Top
Connect
1
👋 Hello
Powered by Joinchat
Hello!! 👋 Manish here, Thanks for visiting The Healthcare IT Experts Blog !! How can i help you?