These steps can help improve your organization’s healthcare data collection practices and go beyond that to facilitate secure sharing and exchange of data.
- Inventory the data you have already collected.
- Identify gaps in your data that could be causing issues with patient care.
- Develop ways to collect that data.
What is the importance of data collection in quality improvement in healthcare?
It’s mind boggling to think the amount of data produced in just the last few years surpasses the amount of data generated in our entire human history, For all this massive amount of data to be functional it needs to be processed, stored, and analyzed.
That is the purpose of data collection. However, modern technologies have struggled to contend with the huge volumes of information, which has led to poor performance, lost revenues, and wasted time. According to Forbes, 95 percent of businesses have problems managing data and are looking for an effective solution.
Data collection in healthcare is the process of collecting, analyzing, and using the data for patient documentation and resources. This technology allows patient data to be immediately available system-wide and the collaborative efforts within any medical system can improve the accuracy of medical data collection.
What are examples of data collection tools in healthcare?
A) Interviews/focus groups b) Phone records c) Surveys/questionnaires d) Medical records (or clinic and office charts) e) Recorded data from EEGs, ECGs, etc.
What are 4 popular data collection techniques?
2. What are the primary data collection methods? – As is well known, gathering primary data is costly and time intensive. The main techniques for gathering data are observation, interviews, questionnaires, schedules, and surveys.
What is the purpose of data collection in health?
Improved Decision-Making with Patient Care – Data collection allows all providers to make more informed decisions about a patient’s care. With applications designed for sharing patient data across multiple channels, every care provider within one organization has all the information available to treat that patient.
What are the 5 methods of collecting data in statistics?
Primary data-collection methods – When the party conducting the research (whether that’s a person or an organization) collects data, it’s considered primary data, as opposed to secondary data, which an external source collects and references. Some of the primary data-collection methods covered below are quantitative, dealing with countable data.
What are the methods of data collection in community health nursing?
The Nursing Process
|Fundamentals of Nursing Practice
|© Rhodora Cruz
CHAPTER 4: The Nursing Process Chapter 4 The Nursing Process The problem solving model for nursing is called the nursing process. The nursing process consists of the assessment, nursing diagnosis, planning, intervention, and evaluation. In the assessment process, you look at the patient from head to toe, and also family, and community support.
Next, you formulate the nursing diagnosis, which is the problem. Then, you plan on the things you should do to resolve the nursing diagnosis. Afterwards, you implement the interventions to the patient. Finally, you evaluate whether what you implemented improved the client’s or patient’s condition or not.
The nursing process is a thoughtful, deliberate use of a problem-solving approach to nursing. This process will form the structure by which you function. You will need to consult a text on nursing theory for a complete understanding of the nursing process.
- Assessment Assessment is the process of gathering information, analyzing information, and identifying problems.
- The basic purpose of some skills is to gather information.
- For every skill that you utilize, however, you must collect appropriate information to implement the skill correctly and safely.
- In addition to carrying out the specific assessment listed, you should always be observant while performing the procedure.
It is an excellent time to gain further information about the patient. You may extend your knowledge of existing problems or gain insight that will lead you to identify new ones (Ellis, Nowlis, Bentz, 1992). Nursing assessments do not focus upon disease, as do medical assessments.
Nursing assessments focus upon a client’s response to a health problem. Nursing assessment should include the clients’ perceived needs, health problems, related experience, health practices, values, and lifestyles. The assessment process involves four closely related activities: collecting data, organizing data, validating data, and documenting data Collecting Data Data collection is the process of gathering information about a client’s health status.
It must be both systematic and continuous to prevent the omission of significant data and reflect a client’s changing health status. A baseline data is all the information about a client; it includes the nursing health history, physical assessment, the physician’s history and physical examination, results of laboratory and diagnostic tests, and material contributed by other health personnel.
- Client data should include past history as well as current problems Types of Data Data can be subjective or objective.
- Subjective data, also referred to as symptoms are apparent only to the person affected and can be described or verified only by that person.
- Subjective data include the client’s sensations, feelings, values, beliefs, attitudes, and perception of personal health status and life situation.
Objective data, also referred to as signs are detectable by an observer or can be tested against an accepted standard. They can be seen, heard, felt, or smelled, and they are obtained by observation or physical examination.
- For example, subjective data is when a patient states “I am hurting.”
- Objective data is when the nurse observes that the patient is grimacing and holding on to his stomach.
- Sources of Data
Sources of data can be primary or secondary. The client is the primary source of data. Family members or other support persons, other health professionals, records and reports, laboratory and diagnostic analyses, and relevant literature are called secondary sources.
- Data Collection Methods The primary methods used to collect data are observing, interviewing, and examining.
- Observation occurs whenever the nurse is in contact with the client or support persons.
- Interviewing is used mainly while taking the nursing health history.
- Examining is the major method used in the physical health assessments.
This will be discussed in detail in Chapter 8. There are four kinds of interview questions, namely, closed or open-ended questions, and neutral or leading. Closed questions used in the directive interview, are restrictive and generally require only “yes” or “no” or short factual answers giving specific information.
Examples of closed questions are “What medication did you take?” “Are you having pain now?” “When did you fall?” The stressed person and the person who has difficulty communicating will find closed questions easier to answer. However, if you need more information, open-ended questions are more appropriate.
They allow clients the freedom to talk about what they wish. Examples are “Tell me about the medication you are taking.” “Tell me about your pain you are having.” “Tell me about the fall.” A neutral question is a question the client can answer without direction or pressure from the nurse.
Examples are “How do you feel about that?” “Why do you think you had the operation?” These type of questions allow the patient to think for themselves. A leading question directs the client’s answer. The phrasing of the question suggests what answer is expected. Examples are “You’re stressed about surgery tomorrow, aren’t you?” “You don’t like the medicine, do you?” These type of questions can create problems if the client, in an effort to please the nurse, gives inaccurate responses.
This can result in inaccurate data. When interviewing, the nurse must schedule the best time and well-lighted, well-ventilated, free of noise environment. A seating arrangement with the nurse behind a desk and the client seated across creates a formal setting.
In contrast, a seating arrangement in which the parties sit on two chairs placed at right angles to a desk or table or a few feet apart, with no table between, creates a less formal atmosphere. The distance between the interviewer and interviewee should be neither too small nor too great, because people feel uncomfortable when talking to someone who is too close or too far away.
Most people feel comfortable maintaining a distance of 3 to 4 feet during an interview. Stages of Interview An interview has three major stages, the opening or introduction, the body or development, and the closing. The opening can be the most important part of the interview because what is said and done at that time sets the tone for the remainder of the interview.
- Its purpose is to establish rapport and trust.
- Example of an opening is “Good morning Mr.
- Jones, I am Judy Oliver, a nursing student,” accompanied by nonverbal gestures, such as a smile, a handshake, and a friendly manner.
- The body of an interview is the part in which the client communicates what he or she think, feels, know, and perceives in response to questions from the nurse.
An example of this interview part is “What brought you to the hospital today?” The closing is the part when the nurse had gathered all the information she requires for the objective part of assessment. The closing is important in maintaining the rapport and trust and in facilitating future interactions.
Signal that interview is coming to an end by offering to answer questions: “Do you have any questions?” Declare completion of task. State appreciation or satisfaction what was accomplished in the interview. Express concern for the person’s welfare and future. Plan for the next meeting, if there is to be one.
Reveal what will happen next. Signal that time is up if a time limit was agreed in the beginning. Finally, provide summary to verify accuracy and agreement. Objective Part of Data Collection The physical examination or physical assessment is a systematic data-collection method that uses observational skills, such as the senses of sight, hearing, smell, and touch, to detect health problems.
- To conduct the examination the nurse uses techniques of inspection, auscultation, palpation, and percussion.
- This will be discussed in Chapter 8.
- Organizing Data The nurse uses a framework to organize the data collected.
- Most schools of nursing and health care providers have developed their own structured assessment tools which can be based on nursing theories.
Example of these nursing models are the Roy Adaptation Model and Orem’s Self-Care Model. Example of nonnursing model is Maslow’s Hierarchy of Needs.
- Nursing Models
- Roy’s Adaptation Model
- 1. Physiologic needs
Activity and rest Nutrition
- Fluid and electrolytes
- Regulation: temperature
- Regulation: the senses
- Regulation: endocrine system
- 2. Self-concept
Physical self Personal self
- 3. Role function
- 4. Interdependence
- Orem’s Self-Care Model
- Universal Self-Care Requisites
1. The maintenance of a sufficient intake of air.2. The maintenance of a sufficient intake of water.3. The maintenance of a sufficient intake of food.4. The provision of care associated with elimination processes and excrement.5. The maintenance of a balance between activity and rest.6. The maintenance of a balance between solitude and social interaction.
- 7. The prevention of hazards to human life, human functioning, and human well-
- 8. The promotion of human functioning and development within social groups in
- accord with human potential, known human limitations, and human desire to
- be normal.
- Nonnursing Models
- Maslow’s Hierarchy of Needs
- These human needs must be met by the client in order of priority.
- First need: Physiologic needs
- Second need: Safety and security needs
- Third need: Love and belonging needs
- Fourth need: Self-esteem needs
- Last need: Self-actualization needs (Kozier, Erb, Berman, and Burke, 2000).
Data for Madeline Sokolsky, Organized According to Roy Adaptation Model
- Physiologic Needs
- Activity and Rest
No musculoskeletal impairment Difficulty Sleeping because of cough
- ·Can’t breathe lying down”
- States “I feel weak”
Short of breath on exertion
- Exercises daily
28 years old, 158 cm (5 ft, 2 in) tall, weighs 56 kg (125 lb) Usual eating pattern “3 meals a day”
- ·No appetite” since having “cold”
- Has not eaten today; last fluids noon
- Decreased skin turgor
- Abdomen soft, nondistended
Decreased urinary frequency and amount X 2 days No difficulty urinating
- Last bowel movement yesterday, formed, “normal”
- Fluid and electrolytes
Last fluids noon approximately 150 cc Nauseated
- No diarrhea
- Decreased urinary frequency and amount x 2 days
- Radial pulse weak and regular, 92
Cough productive of small amounts of pale pink sputum Inspiratory crackles auscultated throughout right upper and lower chest
- Diminished breath sounds on right side
- ·Can’t breathe lying down”
- Short of breath of exertion
- Reports “pain in lungs,” especially when coughing
- Respiration 32, shallow
- Oxygen saturation 89%
Lives in a safe environment Husband supportive
- Decreased skin turgor
- Skin hot and pale, cheeks flushed
- Old surgical scars: anterior neck, RLQ abdomen
- Regulation: Temperature
Oral temp 39.4C (103F) Diaphoretic
- Regulation: the senses
Wears eyeglasses Pupils 3 mm, equal, brisk reaction
- No hearing aids
- Likes perfumes
- Regulation: endocrine system
Menses regular, first day of last menstrual cycle 1 week ago Nauseated
- Physical self
Feels weight is normal
- Personal self
Views self as beautiful Well groomed, says, “Too tired to put on makeup”
- Anxious: “I can’t breathe”
- Role function
Wife and a mother of 3 year old daughter Working mother, attorney
- States “good relationships with friends and coworkers”
- Expresses concerns about work: “I’ll never get caught up”
Husband out of town; will be back tomorrow afternoon Validating Data Validation is the act of double checking or verifying data to confirm that they are accurate and factual. Validating data ensures that assessment information is complete. The objective and subjective data agree.
- You may also obtain additional information that may have been overlooked.
- Validating data is done by comparing subjective and objective data, clarifying ambiguous statements, making sure that the data consist of what the clients says, and by using references, such as textbooks, journals, and research reports.
Documenting Data Accurate documentation is essential and should include all data collected about the client’s health status. Data are recorded in a factual manner and not interpreted by the nurse. The next step of the nursing process after assessment is the nursing diagnosis (Kozier, Erb, Berman, and Burke, 2000).
- Nursing Diagnosis Nursing Diagnosis is the process in which you classify the problem in the assessment phase into an approved classification process called NANDA which stands for North American Nursing Diagnosis Association.
- A nursing diagnosis is a clinical diagnosis made by a registered nurse which, unlike physician’s diagnosis, does not cover the patient’s medical condition, but the patient’s response to the medical condition.
Patients generally have multiple nursing diagnoses covering everything from their physical well-being through their psychosocial well-being to the well-being of their family and caregivers. These diagnoses must cover problems that the nurse can treat independently of the MD.
A complete nursing diagnosis is written in the format problem related to cause of problem as evidenced by symptoms of problem ( retrieved on 07/02/2008). An example of such a nursing diagnosis based on the nursing assessment above would be Impaired gas exchange related to productive cough as evidenced by shallow respiration of 32, oxygen saturation of 89 %, inspiratory crackles auscultated throughout right upper and lower chest, diminished breath sounds on right side and complaint of “can’t breathe lying down” and short of breath of exertion.
You can formulate nursing diagnosis by looking up the NANDA list of nursing diagnosis while basing it on the assessment data acquired. NANDA, the North American Nursing Diagnosis Association, has an approved list of nursing diagnoses which may be used in North America.
|The current (2003-2004) North American List of Approved Nursing Diagnoses
Activity alteration Activity intolerance
- Activity intolerance risk
- Activities of Daily Living (ADLs) alteration
- Acute pain
- Adjustment impairment
- Adolescent behavior alteration
- Adult behavior alteration
- Airway clearance impairment
- Alcohol abuse
- Anticipatory grieving
- Aspiration risk
- Auditory alteration
- Automic dysreflexia
Bathing/hygiene deficit Blood pressure alteration
- Body image disturbance
- Body nutrition deficit
- Body nutrition deficit risk
- Body nutrition excess
- Body nutrition excess risk
- Bowel elimination alteration
- Bowel incontinence
- Breast feeding impairment
- Breathing pattern impairment
Cardiac alteration Cardiovascular alteration
- Caregiver role strain
- Cerebral alteration
- Child behavior alteration
- Chronic low self-esteem disturbance
- Chronic pain
- Colonic constipation
- Comfort alteration
- Communication impairment
- Community coping impairment
- Compromised family coping
- Contraceptive risk
Decisional conflict Defensive coping
- Disabled family coping
- Disuse syndrome
- Diversional activity deficit
- Dressing/grooming deficit
- Drug abuse
- Dying process
- Dysfunctional grieving
- Failure to thrive
Family coping impairment
- Family process alteration
- Fecal impaction
- Feeding deficit
- Fertility risk
- Fluid volume alteration
- Fluid volume deficit
- Fluid volume deficit risk
- Fluid volume excess
- Fluid volume excess risk
- Functional urinary incontinence
Gas exchange impairment Gastrointestinal alteration
- Growth and development alteration
- Gustatory alteration
Health maintenance alteration Health seeking behavior alteration
- Home maintenance alteration
Immunologic alteration Individual coping impairment
- Infant behavior alteration
- Infant feeding pattern impairment
- Infection risk
- Infection unspecified
- Infertility risk
- Injury risk
- Instrumental Activities of Daily Living (IADLs) alteration
- Intracranial adaptive capacity impairment
Kinesthetic alteration Knowledge deficit
- Knowledge deficit of diagnostic test
- Knowledge deficit of dietary regimen
- Knowledge deficit of disease process
- Knowledge deficit of fluid volume
- Knowledge deficit of medication regimen
- Knowledge deficit of safety precautions
- Knowledge deficit of therapeutic regimen
- Latex allergy response
- Medication risk
- Memory impairment
- Musculoskeletal alteration
Nausea Newborn behavior alteration
- Noncompliance of diagnostic test
- Noncompliance of dietary regimen
- Noncompliance of fluid volume
- Noncompliance of medication regimen
- Noncompliance of safety precautions
- Noncompliance of therapeutic regimen
- Nutrition alteration
: The Nursing Process
What is an example of a health related source of data collection?
Registries – Maintaining registries is a method for documenting or tracking events or persons over time (Table 5.4). Certain registries are required by law (e.g., registries of vital events). Although similar to notifications, registries are more specific because they are intended to be a permanent record of persons or events.
What is the top 7 data collection method?
What is a Data Collection Tool? – Data collection tools refer to the devices/instruments used to collect data, such as a paper questionnaire or computer-assisted interviewing system. Case Studies, Checklists, Interviews, Observation sometimes, and Surveys or Questionnaires are all tools used to collect data.
- It is important to decide the tools for data collection because research is carried out in different ways and for different purposes.
- The objective behind data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the posed questions.
The objective behind data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed – Click to Tweet The Formplus’ online data collection tool is perfect for gathering primary data, i.e.
- Raw data collected from the source.
- You can easily get data with at least three data collection methods with our online and offline data gathering tool.I.e Online Questionnaires, Focus Groups, and Reporting.
- In our previous articles, we’ve explained why quantitative research methods are more effective than qualitative methods,
However, with the Formplus data collection tool, you can gather all types of primary data for academic, opinion or product research.
What are the most frequent methods in collecting data?
What are the Most Common Means for Collecting Data? We will define our April focus broadly to include any qualitative or quantitative methods that involve questioning, prompting, or working with participants to collect or generate data. Dr. Zina O’Leary, author of numerous SAGE books including the new, is a Mentor in Residence for April.
Use MSPACE20, for a, Primary data is data collected by researchers expressly for their research purposes – it is data that does not exist independent of the research process. Primary data is current, it is wholly owned by the researcher and, most importantly, it is targeted to specific issues the researcher is exploring.
The most common way to collect primary data is through surveys and interviews. Surveying is the process of collecting data through a questionnaire that asks a range of individuals the same questions related to their characteristics, attributes, how they live or their opinions.
- Interviewing, on the other hand, involves researchers seeking open-ended answers related to a number of questions, topic areas or themes.
- These methods put you, as the researcher, in charge.
- Not only do you get to ask what you want, when you want, you also get to ask it how you want – you get to choose the wording, the order, the prompts, the probes.
Observation studies, a systematic method of data collection that relies on a researcher’s ability to gather data through his or her senses, are similar in that you set up the protocols for data collection – you decide what you will observe, when you will observe it, what you will record as ‘data’.
In all three approaches, data collection is directed with some precision towards your research question, hypothesis, aims and objectives, and this has real appeal. The data collected is not superfluous but is, in fact, custom-built for your research project. But there are some challenges associated with the collection of primary data.
For one, it is a lot of work. Whether it be surveys, interviews or observation studies, it is not easy to design your own research protocols. Survey instruments are notoriously difficult to get right. Getting through a series of interviews and thoughtfully analysing them can be an exercise in frustration.
And observation studies can be complex and leave you with a pile of messy data. There are also a host of ethical issues that you will need to work through to ensure you do no harm to your respondents though your research processes. Primary data collection is also time-consuming, often expensive and doesn’t always go to plan.
Getting enough survey respondents within your timeframe, racing around different parts of the city or state to conduct an interview, and the prolonged engagement that observation sometimes demands – all those need to be factored into the research design decision-making process.
What can we do to improve reliability of data errors in data?
5. Optimize Data Collection Processes – Once you’ve analyzed your internal and external data collection processes, you need to optimize that data collection to minimize the incidence of unreliable data. Here’s how to do that. Start by analyzing your internal processes for entering new data.
What is the purpose of data in quality and improvement processes?
Description – A data quality improvement project concentrates on specific data quality issue(s) that are negatively impacting the organization. The goal is to support business needs by improving the quality of specific data where data quality issues are suspected or already known.
- This can be any set of data – internally created or externally acquired data.
- This type of project selects the applicable steps from the Ten Steps Process to understand the information environment surrounding the data quality issue and business needs, assess the data quality, and show business value.
For the most sustained results, the goal should be to identify root causes and improve the data by preventing the issues from arising again, such as by implementing new or enhancing existing processes to manage the data. Improving the data also includes correcting the current data errors.
To sustain data quality, some of the improvements or controls may be candidates for on-going monitoring. Communicating, managing the project, and engaging with people are done throughout the project. The Ten Steps Process can provide the foundation for the project plan. A variation of this type of project is to use the Ten Steps as the basis for creating a data quality improvement methodology customized to your particular organization.
Read full chapter URL: https://www.sciencedirect.com/science/article/pii/B9780128180150000013
Why is it important to collect high quality data?
Why Is Data Quality Important? – Quality data is key to making accurate, informed decisions. And while all data has some level of “quality,” a variety of characteristics and factors determines the degree of data quality (high-quality versus low-quality).
Because data accuracy is a key attribute of high-quality data, a single inaccurate data point can wreak havoc across the entire system. Without accuracy and reliability in data quality, executives cannot trust the data or make informed decisions. This can, in turn, increase operational costs and wreak havoc for downstream users.
- Analysts wind up relying on imperfect reports and making misguided conclusions based on those findings.
- And the productivity of end-users will diminish due to flawed guidelines and practices being in place.
- Poorly maintained data can lead to a variety of other problems, too.
- For example, out-of-date customer information may result in missed opportunities for up- or cross-selling products and services.
Low-quality data might also cause a company to ship their products to the wrong addresses, resulting in lowered customer satisfaction ratings, decreases in repeat sales, and higher costs due to reshipments. And in more highly regulated industries, bad data can result in the company receiving fines for improper financial or regulatory compliance reporting.
How does data improve the quality of care?
Data collection tools and methods generate information about patients that is supposed to improve the quality of medical services, treatment, and care according to the patient’s needs. The quality of the collected data ensures the competitive advantage of the medical facility.
What is the importance of improving data quality?
Benefits of good data quality – From a financial standpoint, maintaining high data quality levels enables organizations to reduce the cost of identifying and fixing bad data in their systems. Companies are also able to avoid operational errors and business process breakdowns that can increase operating expenses and reduce revenues.
- In addition, good data quality increases the accuracy of analytics applications, which can lead to better business decision-making that boosts sales, improves internal processes and gives organizations a competitive edge over rivals.
- High-quality data can help expand the use of BI dashboards and analytics tools, as well – if analytics data is seen as trustworthy, business users are more likely to rely on it instead of basing decisions on gut feelings or their own spreadsheets.
Effective data quality management also frees up data management teams to focus on more productive tasks than cleaning up data sets. For example, they can spend more time helping business users and data analysts take advantage of the available data in systems and promoting data quality best practices in business operations to minimize data errors.