Chapter 3 - Introduction to Databases and Data Analytics

Question 1

database

Answer 1

an organized collection of locally related data, text, references, or pictures in a standardized format, typically stored in a computer system for multiple uses

Question 2

database administrator (DBA)

Answer 2

the individual responsible for the technical aspects of databases. The DBA is responsible for designing the database as well as managing the database after implementation

Question 3

stewardship

Answer 3

the job of supervising or taking care of something, such as an organization or property

Question 4

certified health data analyst (CHDA)

Answer 4

an AHIMA credential that identifies practitioners who have the knowledge to acquire, manage, analyze, interpret, and transform data into accurate, consistent, and timely information while balancing the healthcare organization’s strategic vision with day-to-day details

Question 5

Triple Aim of Healthcare

Answer 5

improve the patient experience, reduce costs, and improve the health of the general population

Question 6

data set

Answer 6

a collection of data

Question 7

database management system (DBMS)

Answer 7

a software package designed to define, manipulate, retrieve and manage data in a database

Question 8

data definition language (DDL)

Answer 8

the syntax for creating and modifying database objects such as tables, indices, and users. DDL statements are similar to a computer programming language for defining data structures, especially database schemas

Question 9

data manipulation language (DML)

Answer 9

a computer programming language used for adding (inserting), deleting, and modifying (updating) data in a database. A DML is often a sublanguage of a broader database language such as SQL, with the DML comprising some of the operators in the language

Question 10

data structures

Answer 10

a data organization, management, and storage format that enables efficient access and modification; more precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data

Question 11

schema

Answer 11

a representation of a plan or theory in the form of an outline or model

Question 12

data control language (DCL)

Answer 12

a syntax similar to a computer programming language used to control access to data stored in a database

Question 13

natural language queries

Answer 13

the use of common words to tell the database which data are needed. For example, the user may enter a query by typing “list all of the patients whose principal procedure is 0F140D3.”

Question 14

query by example (QBE)

Answer 14

a query method whereby the user only has to point and click to choose tables and fields contained in the database. The IS then allows the user to choose whether the entries that meet those criteria should be included or excluded from the query

Question 15

Boolean data type

Answer 15

a data type that has one of two possible values (usually denoted true and false, but in search queries can include things such as AND, BUT, OR, and NOT to retrieve information based on a specific search statement)

Question 16

wildcard character

Answer 16

a kind of placeholder represented by a single character, such as an asterisk (*), which can be interpreted as a number of literal characters or an empty string. It is often used in file searches so the full name need not be typed

Question 17

structured query language (SQL)

Answer 17

a standard language for the relational database. SQL defines data elements, manipulates data, and controls access. The data definition components of SQL allow the user to create tables, delete tables, and show how something is viewed. Data manipulation allows the user to add and delete rows in a table and to sort, find, and compare. Another function of the data manipulation component is to update data.

Question 18

data dictionary

Answer 18

a descriptive list of the names, definitions, and attributes of data elements to be collected in an IS or a database whose purpose is to standardize definitions and ensure consistent use

Question 19

entity–relationship model (or ER model)

Answer 19

a model that describes interrelated things of interest in a specific domain of knowledge

Question 20

data-flow diagram

Answer 20

a way of representing a flow of data through a process or a system. The DFD also provides information about the outputs and inputs of each entity and the process itself. A data-flow diagram has no control flow, there are no decision rules and no loops

Question 21

audit trail

Answer 21

a data trail that identifies tasks such as who accessed the information system or when and where someone performed a certain data function

Question 22

vocabulary standards

Answer 22

an arbitrary and authoritative list or collection of clinical words or phrases with their meanings; this list addresses the problem of multiple ways to define, classify, and represent language

Question 23

eponym

Answer 23

(1) a person, place, or thing after whom or which someone or something is, or is believed to be, named. (The adjectives derived from eponym include eponymous and eponymic)
(2) a name (as of a drug or a disease) based on or derived from an eponym (e.g. Disneyland as an eponym for Walt Disney)

Question 24

dyspnea

Answer 24

difficult or labored breathing

Question 25

data modeling

Answer 25

The process of determining the users’ information needs and identifying relationships among the data. There are three levels of data models—conceptual data model, physical data model, and logical data model.

Question 26

conceptual data model

Answer 26

not tied to a particular database model, but rather defines the requirements for the database to be developed. This conceptual data model is the basis for the logical and physical data models.

Question 27

physical data model

Answer 27

a model that shows how the data are physically stored within the database.

Question 28

logical data model

Answer 28

a complete representation of data requirements and the structural business rules that govern data quality in support of project’s requirements; in other words, it ensures that the data are available and in a useful format for the intended purpose. For example, the nursing department would look at the data differently than the HIM department and the HIM department would look at it differently than the marketing department.

Question 29

entity-relationship diagram (ERD)

Answer 29

a common type of data modeling that focuses on relationships between entities. An entity is a person, location, thing, or concept that is to be tracked in the database. In healthcare, entities would include such items as patient, physician, and laboratory test. Each entity has attributes, which are facts or data about the entity.

Question 30

use case

Answer 30

a written description of how users will perform tasks on your website. It outlines, from a user’s point of view, a system’s behavior as it responds to a request. Each use case is represented as a sequence of simple steps, beginning with a user’s goal and ending when that goal is fulfilled.

Question 31

relational database

Answer 31

a type of database that stores and provides access to data points that are related to one another; the data is stored as database tables

Question 32

database table

Answer 32

all data related to a particular subject or concept, such as a patient, and stored in rows and columns in a spreadsheet

Question 33

file

Answer 33

a collection of digital data stored in a database

Question 34

data field

Answer 34

A predefined area within a healthcare database in which the same type of information is usually recorded. Examples of data fields collected in healthcare include patient last name, health record number, gender, diagnosis, procedure, and date of birth

Question 35

alphabetic field

Answer 35

a data field that accepts only alphabetic characters. Data elements of this field type include patient name and city.

Question 36

numeric field

Answer 36

a data field that accepts only numbers that can be calculated. This would include charges, but would not include zip codes and the health record number, as these numbers cannot be calculated (added, subtracted, and so on).

Question 37

alphanumeric field

Answer 37

a data field that accepts alphabetic characters, numbers, or a combination of the two. Examples of alphanumeric fields include street address, zip codes, and phone numbers.

Question 38

time and date fields

Answer 38

a data field that contains only a date or time or a timestamp

Question 39

autonumbering fields

Answer 39

a data field that creates a unique number that will never be assigned again. This could be the health record number or a unique number assigned to a patient visit.

Question 40

normalization

Answer 40

breaking the data elements into the level of detail desired by the healthcare organization. For example, last name and first name should be in separate fields, as should city, state, and zip code. This allows the user to search or otherwise manipulate any of the data elements.

Question 41

query

Answer 41

a request for data or information from a database table or combination of tables or from something else

Question 42

key field

Answer 42

a field in a table that holds a unique identifier to ensure that each data entry in the database table is different. This unique identifier is generally called the primary key. In the electronic health record (EHR) and other clinical ISs, this unique identifier is typically the health record number. Within the database, the tables organize the information from all records into rows and columns or cubes and use the primary key to locate the information. With the use of the primary key such as the health record number (in the patient table), then all of the patient’s health information can be linked together.

Question 43

hierarchical database model

Answer 43

a data model that structures the data in a hierarchy very similar to that used for an organizational chart. To use the analogy of a tree, the trunk of the tree would be the starting point or the initial query by the user, and as the search narrows or goes forward, each branch of the tree becomes smaller, progressing outward toward the leaves of the tree, which represent the end point of the search. Each piece of data in the database is called a parent or child node. Pointers indicate where in this tree structure the data are stored.

Question 44

network database model

Answer 44

uses pointers to connect data. The nodes are called owners and members rather than parent and child nodes, as in the hierarchical database model. A member node in the network database model can have more than one owner, unlike in the hierarchical database model. In other words, each piece of data can be connected to multiple pieces of data, kind of like how a room in MajorMUD can be connected to multiple other rooms in different directions (e.g. north, south, east)

Question 45

node

Answer 45

a basic unit of a data structure; nodes contain data and may link to other nodes

Question 46

object-oriented database model

Answer 46

a database management system in which information is represented in the form of objects as used in object-oriented programming

Question 47

multidimensional database model

Answer 47

used in data warehouses. The data are collected from multiple sources, such as other databases, and then summarized. This enables data analysis using a wide range of data. The multidimensional database is designed to quickly access this summarized data

Question 48

data warehouse

Answer 48

a large store of data accumulated from a wide range of sources within a company and used to guide management decisions

Question 49

data repository

Answer 49

an open-structure database (not dedicated to the software of any particular vendor or data supplier) in which data from multiple information systems are stored so that an integrated, multidisciplinary (includes a variety of healthcare providers) view of the data can be achieved in a single source. The data repository is updated by the various ISs in real time, thus providing users with access to the most current information available. This real-time access to data is called online or real-time transaction processing.

Question 50

nosocomial infection

Answer 50

also referred to as healthcare-associated infection (HAI); they are an infection acquired during the process of receiving health care that was not present during the time of admission

Question 51

data mart

Answer 51

a subset of the data warehouse designed for a single purpose or specialized use. The data mart performs the same type of analysis as a data warehouse; however, the scope of the data is narrower. The healthcare organization may choose to develop the data warehouse before the data mart, or the data mart can be developed first, or both can be developed at the same time. This order of development depends on the needs of the organization. Examples of how a data mart may be used include patient satisfaction and medical research. Patient satisfaction would not require the patient-specific information that would be stored in the data warehouse, but it would include the types of services, the nursing unit, and other basic information in addition to the patient survey or other patient satisfaction information collected. The data mart can be used in research because it can be used to provide deidentified information and the limited information required to conduct the research and thus can protect the confidentiality of the patient by providing only the minimum information necessary.

Question 52

data mining

Answer 52

the process of extracting and analyzing large volumes of data from a database for the purpose of identifying hidden and sometimes subtle relationships that would be unnoticed without the analysis. Data mining may also be called database exploration or information discovery.

Question 53

anomaly detection (data mining)

Answer 53

In anomaly detection, the goal is to identify data that does not follow expectations. This can be used to identify fraud and or other issues that need investigation.

Question 54

association rule learning (data mining)

Answer 54

This type of data mining identifies interesting relationships between two concepts in the database. For example, it may identify that patients who are treated with drug A have a better outcome than patients who are treated with drug B.

Question 55

cluster analysis (data mining)

Answer 55

Cluster analysis is identifying concepts that have traits in common. For example, monitoring the treatment practices of physicians for a specific diagnosis or procedure.

Question 56

classification analysis (data mining)

Answer 56

Classification analysis is a method of identifying important information about the data in the database by grouping data much like grouping diagnoses and procedures. This grouped data can then be used in the cluster analysis.

Question 57

regression analysis (data mining)

Answer 57

This method identifies the dependency between two (or more) variables, one being the outcome or dependent variable, the other being the influencing or independent variable. A regression analysis could determine how much influence hypertension or diabetes has on the outcome of a COVD-19 patient’s survival.

Question 58

dependent variable

Answer 58

the variable being tested and measured in an experiment, and that is ‘dependent’ on the independent variable

Question 59

independent variable

Answer 59

the variable in an experiment that is specifically manipulated or is observed to occur before the dependent variable, in order to assess its effect or influence on the dependent variable

Question 60

positive correlation

Answer 60

a relationship between two variables in which both variables move in tandem—that is, in the same direction

Question 61

negative correlation

Answer 61

a relationship between two variables in which one variable increases as the other decreases, and vice versa (i.e. they move in opposing directions)

Question 62

online analytical processing (OLAP)

Answer 62

a data access architecture that allows the user to retrieve specific information from a large volume of data. The use of OLAP turns the data warehouse into a decision support tool because it can analyze large amounts of data quickly. It does this in one of three ways: drilling down into the data, rolling up, or “slicing and dicing.” Drilling down is going deeper and deeper into the data. Rolling up refers to the aggregation of data, which is just the opposite of drilling down. “Slicing and dicing” is looking at the data from multiple viewpoints.

Question 63

Certificate of Need (CON) laws

Answer 63

state regulatory mechanisms for establishing or expanding health care facilities and services in a given area. In a state with a CON program, a state health planning agency must approve major capital expenditures for certain health care facilities, such as extra space for beds.