CHAPTER 16 – Dependability of Nursing Informatics System
CHAPTER 17- Nursing Minimum Data Set
CHAPTER 18- Theories Models and Frameworks
SUBMITTED BY:
Cabanalan, Romar C.
Ladjarani, Sandra H.
Maulana, Dayang Almira H.
Sharif Masdal, Sarah P.
Musa, Shiedjian A.
BSN-IIB
SUBMITTED TO:
Mr. Roderick P. Go
Nursing Informatics Professor
DATE SUBMITTED:
May 10, 2013 CHAPTER 16
Dependability of Nursing Informatics System
Dependable System is the measure of the reliability, integrity and performance of the system. It is defines as trustworthiness of a computing system which allows reliance to be justifiably placed on the service it delivers. It is the collective term used to describe the availability performance and its influencing factors: reliability performance, maintainability performance and maintenance support performance.
In nursing informatics, dependability is the measure of the effectiveness of the health care delivery, quality of service and quality of care through the use of health information system. It encompasses the reliability, integrity, confidentiality and security of the data, information and the process.
Factors that affects dependability of systems
A. Error
In the health care information system, it is the discrepancy between actual results and behavior from the expected or the reference condition or expected performance. In hospitals and clinics, data error or inaccuracy of the information is resulted when an incorrect reading is registered in the measuring equipment, the measuring device is malfunctioning unnoticeably, mishandling of data, incorrect reading of data (honest mistake), the computer system does not properly process the data, a potential bug is existing in the program, the data process by the computer program overflows, incorrect processing of tables and indexes, etc. Hour Systolic Diastolic
H-01 140 110
H-02 145 110
H-03 140 115
H-04 140 110
H-05 135 100
H-06 140 95
H-07 135 95
H-08 135 100
H-09 130 105
H-10 140 110
H-11 110 160
H-12 135 100
H-13 140 100
H-14 145 100
H-15 145 95
Based on the historical pattern, systolic reading is greater than diastolic reading. A diastolic reading of 160 mmHg which is more than the systolic reading of 110 mmHg is an indication that there is an error in reading the data.
B. Fault
Sometimes due to computer bug which is considered a defect in the system. The presence of a fault in health information system may or may not lead to a failure
BSA (m ) Height (cm) Weight (kg)
1.45484 152 50
1.88569 213 60
0.76000 180 52
1.68762 117 58
160006 165 56
1..1276 162 51
1.49276 171 47
Converting the table into chart, we can evidently see that there is an error in the results.
Based on our knowledge, if the height and/or weight is increase there is corresponding increase in the body surface area (BSA), on the contrary, point number 14 of the figure shown below, gives an opposite results. We can say that is an error in processing the data and not the inputting of the data, since the height and weight is correct.
C. Failure
It is a condition in which the system performs unnecessarily or the function is contrary to its specification or the expected condition. An error may not necessarily cause a failure; however a persistent fault can have an impact that mitigates a failure condition. Example is a problem in the network occurs resulting to unavailability of the communication system. This common problem should be anticipated; as such preventive measures and contingency plan should be incorporated in the planning and implementation of the health information.
CHAPTER 17
Nursing Minimum Data Set
Health Information technology offers a large number of benefits to the nurses and healthcare providers as well as the patients and consumers such as the quality and effectiveness of healthcare delivery and service.
Migration from the paper-based to electronic/computer health records minimizes irregularities in the workflow process and decision making process. To accurately and consistently document, accumulate, combined, translate, and retrieve health information data, fields, data set, and data elements must have an equivalent vocabularies and/ or terminologies within the healthcare record before it stores in the database.
The Nursing Minimum Data Set (NMDS) provides a formal structure for electronic healthcare data elements and components to support nursing care in all settings.
What is Minimum Data Set?
The minimum data set providers the specific reference information for the user such as the drug uses, dosage requirement, and direction for use, active ingredients, dates, and other relevant information. The Nursing Minimum Data Set/Elements
The Nursing Minimum Data Set (NMDS) is categorization scheme for the standardization of collection, integration, storage, classification, retrieval and reporting of essential nursing data. The collected data will essentially provide an accurate description of nursing decision, nursing diagnose, nursing care, nursing resources, nursing services, etc. used when providing patient care. The Nursing Minimum Data Set endeavors the standardization of the aggregated of essential nursing data.
Drug Fact
Active Ingredients Purpose Uses
Warnings
Directions
Other Information
Inactive Ingredients
Other Information
Questions/ Comments?
The Nursing Minimum Data Set (NMDS) encompasses three categorical scheme or elements which includes the following:
Nursing Care Elements
Nursing Intervention
Nursing Outcome
Nursing Care Intensity
Patient Demographic Elements
Personal Identification
Date of Birth
Sex
Nationality
Residence
Service Elements
Unique facility or agency number elements
Unique patient health record number
Unique number of principle Registered Nurse
Episode admission
Discharge or termination date
Disposition of patient
Expected payer for medical bill CHAPTER 18
Theories Models and Frameworks
Informatics System in Health Care Industry
Information system is collective term referring to a system of data records and activities that processes and translate the data to information in an automated process.
The processing of data involves the use of computer systems and specialized software that manipulates the information- processing activities of the organization. Computer- based information systems are in the field of information technology. Thus, the discipline of nursing informatics is related to the processing of the data of patients records into information which are supported by information systems.
Information Systems in the Hospital
Transaction processing Information System
Operation System
Individual Patient record
Hospital Information System
Nursing Information System
Pharmacy Health System
Preservation of Healthcare Quality Theory in Nursing Informatics
In the Philippines, many organizations such as Department Of Health are trying to educate and inform the public about health care quality.
The purpose of implementing quality health care is to offer expert advice and suggest noteworthy approaches to solve medical problems, illnesses, sickness, ailment, disorder and complaints.
Notwithstanding the readiness of the solution, the solution typically involves a multitude of strategies including regulatory forms, financial incentives, independent oversight and patient education.
HEALTH INFORMATION TECHNOLOGY (HIT)
Article 8 section 11 of the 1987 constitution of the Philippines
States the “the state shall adopt an integrated and comprehensive approach to
health development which small endeavor to make essential goods, health and
other social services available to all people.”
Health Informatics Paradigm Shift
Basic Steps to properly implement the nursing information system in the Hospital:
Stage 1
3 Translate Data into Information (with
2 statistical processes) Collecting and 1 Analyzing the Data
Commencing the Project
STAGE 2
health care provider are provided simultaneously.
6 Project Evaluation
5
Support from the Client
4
Dissemination of Information
Defining Component of Nursing Informatics
Data
It simply exists and has no significance beyond its existence. It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.
Information, often in the form of facts or figures obtained from experiments or
surveys, used as a basis for making calculations or drawing conclusions.
Information, for example, numbers, text, images, and sounds, in a form that is
suitable for storage in or processing by a computer.
Information
It is a date that has been given meaning by way of relational connection. In computer parlance, a relational database makes information from the data stored within it.
Definite knowledge acquired or supplied about something or somebody.
The collected facts and data about a particular subject.
A telephone service that supplies telephone numbers to the public on request.
The communication of facts and knowledge.
Computer data that has been organized and presented in a systematic fashion to
clarify the underlying meaning
A formal accusation of a crime brought by a prosecutor, as opposed to an
indictment brought by a grand jury.
Knowledge
It is the appropriate collection of information, such that its intent is to be useful.
Knowledge is a deterministic process. When someone "memorizes" information (as less aspiring test bound students often do), then they have amassed knowledge.
General awareness or possession of information, facts, ideas, truths, or
principles.
Clear awareness or explicit information, for example, of a situation, or fact.
All the information, facts, truths, and principles learned throughout time.
Familiarity or understanding gained through experience or study
Understanding
Understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". In computer parlance, Al systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.
Converting Data into Information
Patient data, medical records and nursing documents becomes information when it is applied to some purpose and adds value for the recipient. Process of converting data to information are tabulated below, the first column shows the enormous amount of data that the nurse may process, while the second column shows how the various types of data could be processed to create useful information.
Possible Methods of Converting Patients Data and Medical Data into Information Records Plot charts, create tables and Systolic and Diastolic Readings identify trends Find average, typical readings and Body Temperature variances
Present complex data as a chart Body Mass Index, Bone Mass on graph Density etc Monitor changes over time and Drug Dosage Requirement forecast future values Cost of hospitalization, laboratory Compare figures, identify records, patient mapping similarities and prepare trending Nurses Schedules, Health care Assess whether a result is management significant or occurred by chance Assess whether one thing is Accident records related to another
Collecting data is expensive and to merit the effort, you need to be very clear about why you need it and how you plan to use it. One of the main reasons that organizations collect data is to monitor and improve performance of healthcare delivery and services. Some other data are irrelevant and does not provide accurate benefits in decision making, unless late connections elsewhere are causing the problem. Just as important, the nurses must use the data correctly and processes properly. Wrong interpreting the results may lead to more serious problems.
Example
The date shown below is taken from 120 patient of certain hospital in Manila with the height and weight which will be used to determine the body mass index (BMI).
Height (in) Weight (lbs) Height (in) Weight (lbs)
58.27 91.39 58.27 100.43
59.27 94.40 59.27 104.44
60.28 97.41 60.28 107.46
61.28 100.43 61.28 111.47
62.29 104.44 62.29 115.49
63.29 107.48 63.29 118.50
64.30 110.47 64.30 122.52
65.30 114.49 65.30 126.54
66.31 118.45 66.31 130.49
67.31 121.46 67.31 134.51
68.32 125.47 68.32 138.52
69.32 128.48 69.32 142.54
58.25 96.41 58.25 105.45
59.25 99.42 59.25 109.46
60.26 102.43 60.26 112.48
61.26 106.45 61.26 116.49
62.26 109.46 62.26 120.51 63.27 113.48 63.27 124.53
64.27 116.49 64.27 128.54
65.28 120.51 65.28 132.56
66.28 124.47 66.28 136.51
67.29 127.48 67.29 140.53
68.29 131.49 68.29 144.54
69.29 135.51 69.29 149.56
58.24 129.55 58.24 138.59
59.25 133.57 59.25 143.61
60.25 138.59 60.25 148.63
61.26 143.61 61.26 153.65
Height (in) Weight (Lbs) Height (in) Weight (Lbs)
62.26 147.63 63.23 158.67
63.27 152.65 64.23 163.69
64.27 157.67 65.23 168.71
65.27 162.69 66.24 173.53
66.28 167.51 67.24 178.55
67.28 172.53 68.24 184.53
68.29 177.48 69.25 189.55
69.29 182.50 58.25 119.51
58.21 134.57 59.25 124.53 64.27 145.62 60.26 128.54
65.28 150.64 61.26 132.56
66.28 155.58 62.26 136.58
58.24 110.47 66.26 148.56
59.24 114.49 67.27 153.58
60.25 118.50 68.27 158.43
61.25 122.52 69.27 162.44
62.26 126.54 62.26 142.60
63.26 130.55 63.26 146.62
64.27 134.57 64.27 151.64
65.27 138.59 65.27 156.66
66.27 142.54 66.27 161.50
67.28 146.55 67.28 166.51
68.28 151.57 68.28 171.47
69.29 155.58 69.29 176.48
58.23 115.49 69.29 128.54
59.23 119.51 59.24 133.57
60.24 123.52 60.25 137.58
61.24 127.54 61.25 164.45
62.25 131.56 68.29 169.46
63.25 135.57 58.24 124.53
64.25 140.60 63.27 141.60
65.26 144.61 67.29 159.60 The date shown above can be translated into information shown below
35.0%
65.0%
Upon collecting data to different patients, the nurse can translate the data into information and eventually the information can be converted to knowledge. The nurse may learn that risk dactor for high blood pressure is age, weight, family history and minor factor such as race and sex.
Variable Coefficients
Intercept -15.49
Age 0.077
Race 4.22
Sex 1.5
Change in Weight 0.13
Characteristics of Data Quality
The characteristics of data quality are frequently described in terms of data relevancy, completeness, accuracy, precision, accessibility and timeliness. This is sometimes refer as data integrity.
Data Relevancy
Data are meaningful to the performance of the process or application for which they are collected.
Primary Relevant Data Items
Name Age Sex
Secondary Relevant Data Items
Age Occupation Health Habits Environment
Irrelevant Data Items
Hobbies Favorite Color Organization Legality of Data Collection
Data items should be easily obtained or legal to collect such as weight, height, temperature, systolic and diastolic reading. The data can convert or translated to information and eventually to knowledge or can be coupled to other disciplined to concretize the knowledge.
Data Comprehensiveness
All the data items required should be included. the data collection scheme should anticipate future data needs; as such flexibility of data is essentials.
Data Appropriateness
Data attributes and their values should be defined at the correct level of details.
Data Timeliness
Data gathering has to be done on the real-time so that immediate preparedness will be applied to unaffected areas or community and proper response from the health care provider are provided simultaneously.
Data Consistencies and data Uniqueness
There should be no overlapping entities, the values should be the same and consistent within the hospital and clinic department and outside the hospital. The data should also be consistent to the other health institutions, regulatory and policy makers, schools and universities, health agency, etc.
Converting Information to knowledge
The tremendous amount of information that is translated is only usefu; if it can be applied to create knowledge that is significant to the nurse and the healthcare giver.
Building and managing knowledge is one of the greatest challenges that have to establish. Information on its own will not create a knowledge-based system but as a guide in the decision making, we can simply conclude that information and knowledge is the building blocks of the decision making process. The right information fuels the development of intellectual capital which in turns drives innovation and performance improvement.
Example
The statistical information system can interpret the results as follows;
Change in blood pressure = -15.49 + 0.077 (age) + 4.22 (race) + 1.5 (sex) + 0.13
(change in weight).