# A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Numbers

A

  1. Algorithm: a process, set of rules, or well-defined instructions for computer systems to follow in specific calculations or use in solving particular problems.
  2. Artificial intelligence(AI): the simulation of human intelligence processes by machines toward performing tasks that typically require human intelligence.
  3. Augmented intelligence: refers to artificial intelligence systems in assistive roles to enhance human intelligence, rather than replace it, in making decisions. Also sometimes called cognitive computing.
  4. Augmented reality:
  5. Artificial neural networks (ANNs): these are complex computational models inspired by the structure and functions of the human brain nervous system or neural networks, capable of ‘learning’ and pattern recognition and form the basis of every deep learning architecture. They are also known as neural networks (NNs) or neural nets.

B

  1. Big data: data that is so large, increasingly being generated, and complex, that is impossible to process using conventional data processing methods.
  2. Blockchain: a shared/decentralized, immutable distributed ledger that utilizes distributed ledger technology(DLT) in facilitating the process of recording transactions and tracking assets in a network.
  3. Blockchain-as-a-service: a cloud computing service model that offers on-demand access to blockchain development tools to build blockchain projects on the cloud.

C

  1. Clinical Decision Support System (CDSS): these are health information technologies that assist health professionals in managing patients.
  2. Cloud infrastructure: the hardware and software components needed for proper implementation of a cloud computing model.
  3. Cloud computing:
  4. Cloud service providers (CSPs): also known as cloud vendors, they are companies offering cloud services to businesses and organizations. Examples include Amazon AWS, Microsoft Azure, etc.
  5. Computer-Assisted Coding (CAC): a technology that applies natural language processing (NLP) to medical data within electronic health records(EHRs) to extract and match medical diagnoses, procedures and phrases, etc to ICD-10 and CPT codes.
  6. Computer vision: this is a field of artificial intelligence that refers to technologies that enable computers to ‘see’ and understand digital images and videos, draw meaningful inferences from them and thereafter take actions or make recommendations based on the inferences.
  7. Containers: they are software packages that contain the necessary elements to run applications in any environment without middleware. They differ from virtual machines(VMs) in that they achieve virtualization at the level of the operating system and are lightweight using a fraction of the memory VMs require.
  8. Cognitive computing: intelligent decision-making aids that mimic and augment human thought processes, collaborating with humans to help make better decisions. Also sometimes called augmented intelligence.
  9. Current Procedural Terminology (CPT): a medical set of codes that are used to report medical, surgical, and diagnostic procedures and services within the healthcare industry to stakeholders like health insurance companies, accreditation organizations, physicians etc.
  10. Cyberattack: A cyberattack is a malicious and deliberate attempt by an individual or organization to breach the information system of another individual or organization which may involve stealing, exposing, altering, disabling, or destroying information.

D

  1. Data: a representation of facts, concepts, or instructions stored in a computer in a digital format that convey information or a collection of symbols that may be further interpreted.
  2. Data analysis: examining data sets to find trends, similarities, and differences within the datasets and drawing conclusions based on the information derived from the data.
  3. Data analytics: the series of processes involving gathering, cleaning, and processing raw data, towards deriving actionable, relevant information that helps to make informed decisions. Data analysis is a part of data analytics.
  4. Data management: the process of collecting, storing, organizing, and maintaining data in an organization in order to optimize the use of the data within the bounds of policy and regulation so that it can be used to make decisions and take actions that maximize benefits to the organization.
  5. Data science: a multidisciplinary field that uses advanced statistics and mathematics, machine learning and other big data technologies to process large datasets in order to interpret and derive information from the datasets, automate data processes and make future predictions. Data analytics is a part of data science.
  6. Data silo: a collection of data that are kept apart, and not easily or fully accessible by others.
  7. Data privacy: the control users/patients have over how their personal information is processed – collection, storage, sharing, and use, in compliance with data protection laws, regulations, and general privacy best practices.
  8. Database: an organized collection of structured data typically stored electronically in a computer system and by a database management system (DBMS).
  9. Decentralization: the transfer of control of an activity, especially critical or decisive activities, from a single location/authority to several local locations/offices.
  10. Decentralized applications (dApps): digital applications or programs that exist and run on a blockchain or peer-to-peer (P2P) network of a decentralized environment and are free from control and interference by any single authority.
  11. Deep learning: a subset of machine learning based on layers of artificial intelligence neural networks that attempts to simulate the human brain in order to learn from big data.
  12. Distributed autonomous organizations (DAOs): groups of people with no central leader or authority organized on a blockchain who use cryptocurrency as a funding mechanism and make critical decisions by voting.
  13. Distributed ledger technology (DLT): a digital system for recording the transaction of assets in which the transactions and their details are recorded and every participant on the network has an updated synchronized copy of all transactions, as opposed to having a central data store.
  14. Domain expertise: knowledge and understanding of a particular field, profession, or activity such as healthcare, data science, etc., in contrast to general knowledge.

E

  1. Electronic health records (EHRs): a type of health information technology (HIT) designed for the record-keeping of patients’ health information and for easy sharing with other authorised individuals or health facilities.
  2. Electronic medical records (EMRs): a type of health information technology (HIT) designed majorly for the in-facility keeping of patients’ health information and not necessarily for sharing.
  3. Extended reality:

F

G

H

  1. Health informatics:
  2. Health information technologies (HIT): these are the technologies that health organizations employ in the management of health data.
  3. Health data: any information, recorded in any form, related to the health conditions of an individual or population.

I

  1. Information technology(IT): the use of computer and telecommunication systems, software, networks, and infrastructure to create, process, store, retrieve, and share data and information in various forms.
  2. Infrastructure as a Service (IaaS): a cloud computing service model that provides on-demand access to computing resources including storage, servers, network, etc.
  3. International Classification of Diseases, Tenth Revision (ICD-10): a globally used classification system of diagnosis codes representing conditions and diseases, related health problems, abnormal findings, signs and symptoms, and injuries. It is used in epidemiology, clinical reporting, medical claims reporting, and health data management.
  4. Interoperability: the ability of computer systems or software to seamlessly exchange and readily make use of information from another different system.

J

K

L

  1. Labelled (training) data: pieces of raw data that have been tagged with one or more meaningful or informative labels identifying certain properties or characteristics or providing context.

M

  1. Machine learning:
  2. Metadata: a set of data that describes and provides information about other data.
  3. Mixed reality:

N

  1. Natural Language Processing(NLP): a field of artificial intelligence that involves a machine’s ability to decipher and understand human language as it is spoken and written. and offer a response/result.
  2. Neural networks: a field of artificial intelligence that involves a set of algorithms modelled after the human brain, that endeavours to identify underlying relationships in a set of data. They are also known as artificial neural networks (ANNs) or neural nets.

O

  1. On-demand: to scale up and shrink down resources based on demand or on an as-needed basis.

P

  1. Patient portals: secure and save online accesses patients have to their data in the health providers’ information systems to carry out activities like booking an appointment, managing health insurance, etc.
  2. Peer-to-peer(P2P): a decentralized platform or network whereby two individuals exchange some assets e.g. data, digital currency, etc directly with each other, without the involvement of an intermediary or a third party.
  3. Personal health record (PHR): a record of a patient’s own health information collated, managed, and shared by the patient.
  4. Platform as a Service (PaaS): a cloud computing service model that provides developers with on-demand access to a platform for the development of business applications and software.
  5. Programming: the process or activity of writing computer programs that instruct the computer to perform some tasks.

Q

R

S

  1. Semi-structured data: these are unstructured data that have metadata which provides more information about the unstructured data, making them easier to work with compared to purely unstructured data.
  2. Serverless computing: a cloud computing model that offloads all the backend infrastructure management tasks to CSPs.
  3. Singularity: a hypothetical future where the growth of technology is advanced, out of control, and irreversible, in a way that artificial intelligence transcends human intelligence going on to radically and unpredictably transform human reality.
  4. Software as a Service (SaaS): a cloud computing service model that provides end-users with software applications as a service over the internet, also known as cloud-based applications.
  5. Structured data: data in their organised and simplest form, usually arranged neatly in databases.

T

  1. Tacit knowledge: this refers to knowledge acquired from personal experiences and not specifically or directly taught.
  2. Telehealth: access to any healthcare services remotely.
  3. Telemedicine: the remote diagnosis and treatment of patients by means of technology.

U

  1. Unstructured data: completely unorganized and complex data, with no clear format to work with. They form the bulk of big data.

V

  1. Virtual machines (VMs): they are the product of virtualizing a computer system and each run independently with its own operating system. They are similar to containers but differ in that they virtualize at the hardware level and duplicate services for each application they run while containers virtualize at the level of the operating system and do not duplicate application services.
  2. Virtual reality:
  3. Virtualization: a process that uses software to create an abstraction layer over computer hardware, dividing the components (processors, memory, storage, etc.) into multiple virtual environments/computers, each with its virtual processors, memory, storage, etc. This provides a virtual isolated environment to run applications. Virtual machines and containers are both different techniques to achieve virtualization.

W

X

Y

Z

  1. Insurtech –
  2. Digital health –
  3. Healthcare IT –
  4. Big data: A massive volume of structured and unstructured data that is too large to process using traditional database and software technologies.
  5. Big data analytics: The process of collecting, organizing, and synthesizing large sets of data to discover patterns or other useful information.
  6. Datacenter: Physical or virtual infrastructure used by enterprises to house computers, storage, and networking systems and components for the company’s IT needs.
  7. Data integrity: The validity of data, which can be compromised in a number of ways including human error or transfer errors.
  8. Data miner: A software application that monitors and/or analyzes the activities of a computer, and subsequently its user, to collect information.
  9. Data mining: A class of database applications that look for hidden patterns in a group of data that can be used to predict/anticipate future behaviour.
  10. Data warehouse: A data management system that uses data from multiple sources to promote business intelligence.
  11. Database: A collection of data points organized in a way that is easily manoeuvred by a computer system.
  12. Metadata: Summary information about a data set.
  13. Raw data: Information that has been collected but not formatted or analyzed.
  14. Structured data: Any data that resides in a fixed field within a record or file, including data contained in relational databases and spreadsheets.
  15. Unstructured data: Information that does not reside in a traditional column-row database like structured data.
  16. Machine data: This is data that is produced wholly by machines, without human instruction. An example of this could be call logs automatically generated by your smartphone.
  17. Metadata: This is a form of data that provides information about other data, such as an image. In everyday life you’ll find this by, for example, right-clicking on a file in a folder and selecting “Get Info”, which will show you information such as file size and kind, date of creation, and so on.
  18. Real-time data: This is data that is presented as soon as it is acquired. A good example of this is a stock market ticket, which provides information on the most-active stocks in real time.
  19. Quantitative data: otherwise known as structured data— may appear as a “traditional” database—that is, with rows and columns.
  20. Qualitative data: otherwise known as unstructured data—are the other types of data that don’t fit into rows and columns, which can include text, images, videos, and more.
  21. Data integrity:
  22. Data literacy:
  23. Immutable: the content of blockchain blocks is rigid and unchangeable.

 

 

 

 

 

  • Model: Also known as a “hypothesis”, a machine learning model is the mathematical representation of a real-world process. A machine learning algorithm along with the training data builds a machine learning model.
  • Feature: A feature is a measurable property or parameter of the data set.
  • Feature Vector: It is a set of multiple numeric features. We use it as input to the machine learning model for training and prediction purposes.
  • Training: An algorithm takes a set of data known as “training data” as input. The learning algorithm finds patterns in the input data and trains the model for expected results (target). The output of the training process is the machine learning model.
  • Prediction: Once the machine learning model is ready, it can be fed with input data to provide a predicted output.
  • Target (Label): The value that the machine learning model has to predict is called the target or label.
  • Overfitting: When a massive amount of data trains a machine learning model, it tends to learn from the noise and inaccurate data entries. Here the model fails to characterize the data correctly.
  • Underfitting: It is the scenario when the model fails to decipher the underlying trend in the input data. It destroys the accuracy of the machine learning model. In simple terms, the model or the algorithm does not fit the data well enough.
    Here’s a video that describes step by step guide to approaching a Machine Learning problem with a beer and wine example:

 

 

 

 

 

Other as-a-service models include:

  1. Artificial Intelligence as a Service (AIaaS): Allows developers to experiment with AI and test different machine learning algorithms
  2. Backend as a Service (BaaS): Backend cloud storage and processing for faster application development.
  3. Blockchain as a Service (BaaS):
  4. Cloud as a Service (CaaS): an all-in-one service offering that combines IaaS, PaaS, and SaaS in one subscription. Also known as cloud services.
  5. Containers as a Service (CaaS): a complete runtime environment hosted in the cloud that offers containers.
  6. Commerce as a Service
  7. Communications as a Service
  8. Compiler as a Service
  9. Compliance as a Service
  10. Content as a Service
  11. Data as a Service (DaaS): When data is siloed off, it’s not working as hard as it could. By centralizing data in the cloud, it can be accessed easily and analyzed far more deeply.
  12. Device as a Service (DaaS): Companies pay a monthly fee to lease the latest hardware, along with ongoing management and support. A recurring revenue opportunity for MSPs—or a chance for hardware vendors to disintermediate them? HP, for one, offers device aaS as a channel program, and Lenovo is also getting in on the action. Seems most vendors will. See Surface as a service.
  13. Database as a Service (DaaS)
  14. Disaster Recovery as a Service (DRaaS): Leaps into action in the event of a catastrophe to repopulate your network’s data, infrastructure, and applications—ideally before your users even notice a hiccup. More robust than backup aaS.
  15. Environment as a Service (EaaS): Going further than virtual machines or containers, provides test management, test case development, and test execution.
  16. Framework as a Service (FaaS): A service offering that falls between Software as a Service and Platform as a Service—it’s a software framework that provides a customizable foundation for developing apps or systems. It’s not a finished product like SaaS, but it requires less work to implement than a PaaS system.
  17. Hardware as a Service (HaaS)
  18. IoT as a Service (IoTaaS): A “pay as you go” service for IoT devices, so you only use the devices and resources you need at the time. IoTaaS is great for scenarios where there are sudden spikes in customer need and you need to quickly set up and tear down resources and don’t want to incur costs for devices you only need for a short period of time.
  19. Knowledge as a Service (KaaS): A computing service that delivers knowledge—which is data with context—to users, as opposed to just data or information.
  20. Location as a Service (LaaS): Retail (and other) companies sit on an enormous quantity of customer location data without the tools to pull business insight from it. Location aaS lets them rent high-quality location data analysis.
  21. Monitoring as a Service (MaaS): Oversees from the cloud how IT infrastructure, systems, and apps are running. Avoids having to purchase and install a potentially costly on-premises monitoring tool.
  22. Management as a Service
  23. Messaging as a Service
  24. Metal as a Service
  25. Mobile Backend as a Service
  26. Network as a Service (NaaS): Rented network functionality from a third party that owns the infrastructure, usually an ISP. Scale up or down on port capacity as needed—works best for companies with highly variable demand.
  27. Operations as a Service (OaaS): Third-party services that help businesses design, build, maintain, and monitor the IT infrastructure of their dreams. A new name for a managed service you’ve likely been offering for years.
  28. Ransomware as a Service (RaaS): DIY ransomware kits that would-be criminals can purchase and implement. One service you don’t want to deliver. Unfortunately, there’s not a whole lot you can do about the sale of code kits online. What you can do? Protect against ransomware when it attacks.
  29. Security as a Service (SaaS): Outsourced management of a company’s network security and data regulation/compliance to a third party—like an MSSP (managed security service provider).
  30. Storage as a Service
  31. Unified Communications as a Service (UaaS): Managed and hosted communications channels. VoIP, instant messaging, LinkedIn, Skype, phones, Wi-Fi, social media… new communication channels appear at ever-shorter intervals and businesses struggle to keep their networks organized, secure, and efficient. Unified communications aaS vendors take care of all the hardware and software while guaranteeing a level of quality.
  32. Video as a Service (VaaS): Cloud-hosted video conferencing. As more companies move from phone to video conferencing, the IT headaches of keeping them running multiply. Companies moving to cloud-based video and enjoy higher-quality images and fewer dropped calls, with technicians on standby to keep things running smoothly.
  33. Virtualization as a Service (VaaS): One distant server, many accessible virtual machines. Ten years ago, turning one physical server into several virtual machines was a groundbreaking way to fully use server capacity and free up physical space. Today, it can be done via the cloud.
  34. Workspace as a Service (WaaS): Virtual desktop environments. Just log in to access your office desktop, exactly as you like it, with all the business data and applications you need, from any device you choose. Easy to see why this sector has taken off, especially among companies with remote workers and small businesses without resources to efficiently manage their own IT.
  35. Anything (or everything) as a Service (XaaS)