EHR Database and Data Management

EHR Database and Data Management

Directions:
For this assignment, write a 1,000 word paper in which you:

  1. Select a clinically based patient problem in which using a database management
    approach provides clear benefit potential. (Diabetes Type 2).
  2. Consider how a hypothetical database could be created to assist with this clinically
    based patient problem. Identify and describe the data needed to manage this patient
    problem using information from the electronic health record (EHR).
  3. Include a brief description of the patient problem that incorporates information
    needed to manage the specific problem. Describe what information is required for the
    patient to manage the condition and how the database and health care provider can be
    incorporated into the approach for better health outcomes.
  4. Describe each entity (data or attribute) that will be pulled from the EHR as either
    structured or unstructured and provide an operational definition for each. Structured data
    is more easily searchable and specifically defined. For example, structured data can be
    placed in a drop-down menu like hair color: brown, black, grey, salt and pepper, blonde,
    platinum, etc. Unstructured data is data that would be included in a nurse’s notes. An
    operational definition is how a researcher or informatics specialist decides to measure a
    variable. For example, when the nurses enter height into the EHR, do they enter height as
    measured in inches or centimeters or in feet and inches?
  5. Provide a complete description of data entities (the objects for which you seek
    information, i.e., patients) and their relationships to the attributes collected for each entity
    (data collected for each entity, i.e., gender, birthdate, first name, last name, etc.) that apply
    to the hypothetical database. You can use a concept map similar to the “Database Concept Map”
    resource, to help you describe the relationships between each entity and its attributes.

EHR Database and Data Management

Diabetes is a global health problem affecting an unusually high number of people and
with a very high rate of prevalence. It is a persistent disorder that involves high blood pressure in
the body as a result of either due to lack of sufficient level of insulin production or failure of
cells to respond sufficiently to the insulin produced. Type 2 diabetes is characterized by the
insufficient production of insulin and lack of body tissue’s ability to satisfactorily respond to
insulin production. This type of diabetes is the most common accounting for 90% of all diabetes
patients (Heidelbaugh, 2014). It is therefore critical for medical professionals, particularly nurses
to efficiently assist patients suffering from the disease. One such way is to use a well-thought
database, which will provide an excellent platform for providing health care services. The
database will offer some benefits including significantly improving care processes, delaying
diabetes complications due to early and proper interventions and saving health care dollars both
for individuals and the government (Baldo, Lombardi, Cocchio, et al., 2015).

Hypothetical database

The database to be created will be a relational database mainly due to its ability to be
flexible, making it possible to link various tables. The objective of the hypothetical database is to
ensure there are no data redundancy as well as ensuring data integrity and accuracy.
The first step in creating the database is to define the purpose of the database. In this
case, it’s a Type 2 diabetes database where necessary information about the patients, including

EHR DATABASE AND DATA MANAGEMENT 2
their histories, available medication, and management strategies among others are stored
(Eggleston & Klompas, 2014). The data collection, collation of tables with specific primary keys
then follows. The data collected is purely aimed at supporting the objective or purpose of the
database. Ideally, the data will be divided into-subject-based tables, each having a well-thought-
out primary key. For instance, tables having the names and gender of the patients as well as
another that have their residential addresses, they must have primary keys such as patient’s
unique identification number. In reality, creating the relationship between tables is the third step.
The relationship between tables takes any of the various forms such as one-to-many, many-to-
many or one-to-one. In creating the database, proper column data types must be used. For
instance, where dates are involved, the appropriate data type that will be used is date or time.
Additionally, the most commonly used data types in databases are integers, strings, and binary
floating-point numbers.
The fourth step will be refining and normalizing the design. This will be accomplished by
adding more column, creating new tables where necessary to allow optional data, splitting tables
into smaller tables among others.
Eggleston & Klompas (2014) observed that the types of data to be collected and stored in
the database rage from demographic address, and health-related data. Demographic data include
the patient’s full names, gender, age (date of birth), race, level of education, employment status,
economic status, marital status, religion, sexual orientation to mention but a few. Address data
will include the place of residence, which will entail the state, county, street, and telephone
number both for home and work and email address. Health-related records will constitute the
type of diabetes the patient is suffering from, year of diagnosis, types of treatment, strategies
used for managing the condition, blood pressure, weight, physical exercise, caregivers and their
contact information (Eggleston & Klompas 2014). Other medical conditions, which may include
an array of diseases, allergic reaction to medication and food, previous medical history including
treatment among others.

Patient Problem

Type 2 diabetes has been associated with some health-related problems. These include
amputation, cardiovascular diseases, blindness, depression, chronic kidney diseases, high
treatment costs and increased dependency (Heidelbaugh, 2014 and Shah, Langenberg,
Rapsomaniki, et al., 2015). In addition to treating type 2 diabetes, other management strategies
can be used to help patients successfully manage the condition. According to Nyenwe, Jerkins,
Umpierrez & Kitabchi, (2011), these include;

  1. Improving the diet one takes by ensuring its balanced contributes to the successful
    management of the condition. A typical meal should include fresh or frozen fruit and
    vegetables, whole grains, beans, lean meats, and low-fat or fat-free dairy, less starchy
    foods.
  2. Losing weight particularly belly fat is critical in helping lower the glucose levels.
  3. Engaging in regular physical exercise also contributes to lowering glucose levels.
  4. Controlling sleep apnea can attain managing sugar spikes and dips.
    The information can be included in the database. Particularly, the diet that is recommended
    for this group of patients must be included in the database. Together with information on
    patients’ allergies with regards to food, a nurse would be able to reliably inform the patient the
    type of food he needs to consume to successfully manage his or her condition.

Data entity and attributes

EHR DATABASE AND DATA MANAGEMENT 3
By definition, an entity is anything in the real world. It may be an object with a physical
presence or an object with an intangible presence. In the case of the hypothetical database, the
entities include,
Patient identification number; this entails a unique ID that will be adapted to identify all
type diabetic patients uniquely.
Patient medical history; this entails existing medical reports of about the patients. For
instance previous histories of heart complications, genetic problems, allergies to medication and
food
Patient address; this includes details of the patients’ residential information such as the
state of residence, county, street, postal address, among others.
Demographic information; this usually includes general information about the patient.
For instance, gender, date of birth, first name, last name, level of education, economic status,
among others.
Treatment options for patients are another entity. In practice, type 2 diabetic patients are
assisted to manage the condition through a number of ways. For instance, there are medications
that can be used such as metformin (Glucophage, Glumetza), sulfonylureas, meglitinides,
thiazolidinediones, and DPP-4 inhibitors among others. Management strategies such as healthy
eating, physical activities, monitoring blood sugar.
The operational definition of the variables will be based on the standardized measurement
units in the country. The measurements ought to be globally acceptable. For instance, the weight
will be measured in kilograms, height in feet and then converted to meters for the purposes of
measuring body mass index, which will be represented in kg/m 2 , blood sugar will be measured in
mg/dL (milligrams per deciliter. Other important measurements include cholesterol measured in
mg/dl, blood pressure measured in millimeters of mercury (mmHg), microalbumin measured in
mg/mmol.
Ideally, some general information about the patient will be structured, for instance,
gender, unique identification number, religion, ethnicity or race, economic status, level of
education, the type of diabetes, current management strategies employed. However, there are
certain attributes that ought to be unstructured, for instance, the types of diet such patients should
consume, weight in kilograms, height in meters, blood pressure (Eggleston & Klompas 2014).
On the other hand, the entities previously described have characteristics that define them
(attributes). In any database management system and for effective results, there must be a
relationship between the entities and their attributes. This can be shown in the table below.
Entity Attributes
Patient Age, names, birth date, sex, security number,
address, religion, level of education, marital
status,

Medical data and history Genetic problems, allergies to medication and
food, height, weight, temperature, sugar levels,
Treatment and management options Metformin (Glucophage, Glumetza),

sulfonylureas, meglitinides, thiazolidinediones,
and DPP-4 inhibitors among others.
Healthy eating, physical activities, monitoring
blood sugar.

EHR DATABASE AND DATA MANAGEMENT 4

Conclusion

The idea of creating a relational database is important in supporting nurses to continue to
offer patients with quality and timely responsive health care services. Using the case of type 2
diabetes patients, the steps to be followed in creating such a database has been presented. The
health care problem relating to the disorder, which includes high treatment cost, amputation,
depression, blindness among others are succinctly presented. Lastly, the data entities and related
attributes and their structures are also explained.

EHR DATABASE AND DATA MANAGEMENT 5

References

Baldo, V., Lombardi, S., Cocchio, S. et al., (2015). Diabetes outcomes within integrated
healthcare management programs. Prim Care Diabetes, 9:54–59.
Eggleston, E. & Klompas M. (2014). Rational use of electronic health records for diabetes
population management. Curr Diab Rep., 14:479.
Heidelbaugh, J. (2014). Type II diabetes mellitus: A multidisciplinary approach. Amsterdam:
Elsevier.
Nyenwe, E., Jerkins, T., Umpierrez, G. & Kitabchi, A. (2011). Management of type 2 diabetes:
evolving strategies for the treatment of patients with type 2 diabetes. Metabolism, 60(1):
1–23.
Shah, D., Langenberg, C., Rapsomaniki, E., et al., (2015). Type 2 diabetes and incidence of a
wide range of cardiovascular diseases: a cohort study in 1· 9 million people. The Lancet,
385, p. S86.

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