The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
Presentation for UP MSHI HI201 Health Informatics class under Dr. Iris Tan and Dr. Mike Muin. Check out my blog - http://jdonsoriano.wordpress.com/2014/10/09/fitting-the-pi…making-it-work/
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
Presentation for UP MSHI HI201 Health Informatics class under Dr. Iris Tan and Dr. Mike Muin. Check out my blog - http://jdonsoriano.wordpress.com/2014/10/09/fitting-the-pi…making-it-work/
Drug Utilization in a regulated EnviormentAlok Anand
Tracking drugs across the supply chain in a regulated environment. This white paper brief on would be drug utilization approach of Life Science Industry. This white paper is just a step forward to show future life science industry process automation
Developing Drugs in the New Era of Personalized Medicines by Anita Nelsen - Head of Genomic Medicine, Sr. Director, PAREXEL International and Dr. Sy Pretorius - Sr. Vice President, Chief Scientific Officer, PAREXEL International
Real-world patients have an average of 6 serious co-morbid conditions & take 10 medications
*Complicated patients are invariably excluded from clinical research studies, making it impossible to know what treatments work best
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
This paper describes a study of the adoption of Picture Archiving and Communication Systems(PACS). The objective of this study is threefold. First, the adoption rate of PACS by European hospitals is described in relation to the use of other medical information systems. From this, a Medical Information Systems Maturity Scale (MISIS) for hospitals is statistically constructed. The second objective is to identify the key determinants of a hospitals’ score on MISIS, i.e. analyzing the situationality of the scale. The final objective of the paper is to explain and the variation in Medical Information System Maturity among hospitals in Europe. Using the results of this empirical analysis we set out general guidelines for the evolution of PACS maturity [1] within hospitals, based on principals of strategic alignment and situational growth.
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
This Journal publishes original research work that contributes significantly to further the scientific knowledge in pharmacy.
LIVER DISEASE PREDICTION BY USING DIFFERENT DECISION TREE TECHNIQUESIJDKP
Early prediction of liver disease is very important to save human life and take proper steps to control the
disease. Decision Tree algorithms have been successfully applied in various fields especially in medical
science. This research work explores the early prediction of liver disease using various decision tree
techniques. The liver disease dataset which is select for this study is consisting of attributes like total
bilirubin, direct bilirubin, age, gender, total proteins, albumin and globulin ratio. The main purpose of this
work is to calculate the performance of various decision tree techniques and compare their performance.
The decision tree techniques used in this study are J48, LMT, Random Forest, Random tree, REPTree,
Decision Stump, and Hoeffding Tree. The analysis proves that Decision Stump provides the highest
accuracy than other techniques
The Relationship Between Quality of Care and Choice of Clinical Computing System: Retrospective Analysis of Family Practice Performance Under the UK Quality and Outcomes Framework
Physician resistance as a barrier to implement clinical information systems b...Healthcare consultant
In summary, physician resistance toward implementation of clinical information systems is a major barrier. Strategies acknowledged under various categories to overwhelm this barrier provide hope to organizations that are eager to take on this adventure. Decision makers carry the chief responsibility to put in advance clear rules to facilitate physicians’ participation in the implementation process to eliminate their later on opposition.
Drug Utilization in a regulated EnviormentAlok Anand
Tracking drugs across the supply chain in a regulated environment. This white paper brief on would be drug utilization approach of Life Science Industry. This white paper is just a step forward to show future life science industry process automation
Developing Drugs in the New Era of Personalized Medicines by Anita Nelsen - Head of Genomic Medicine, Sr. Director, PAREXEL International and Dr. Sy Pretorius - Sr. Vice President, Chief Scientific Officer, PAREXEL International
Real-world patients have an average of 6 serious co-morbid conditions & take 10 medications
*Complicated patients are invariably excluded from clinical research studies, making it impossible to know what treatments work best
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
This paper describes a study of the adoption of Picture Archiving and Communication Systems(PACS). The objective of this study is threefold. First, the adoption rate of PACS by European hospitals is described in relation to the use of other medical information systems. From this, a Medical Information Systems Maturity Scale (MISIS) for hospitals is statistically constructed. The second objective is to identify the key determinants of a hospitals’ score on MISIS, i.e. analyzing the situationality of the scale. The final objective of the paper is to explain and the variation in Medical Information System Maturity among hospitals in Europe. Using the results of this empirical analysis we set out general guidelines for the evolution of PACS maturity [1] within hospitals, based on principals of strategic alignment and situational growth.
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
This Journal publishes original research work that contributes significantly to further the scientific knowledge in pharmacy.
LIVER DISEASE PREDICTION BY USING DIFFERENT DECISION TREE TECHNIQUESIJDKP
Early prediction of liver disease is very important to save human life and take proper steps to control the
disease. Decision Tree algorithms have been successfully applied in various fields especially in medical
science. This research work explores the early prediction of liver disease using various decision tree
techniques. The liver disease dataset which is select for this study is consisting of attributes like total
bilirubin, direct bilirubin, age, gender, total proteins, albumin and globulin ratio. The main purpose of this
work is to calculate the performance of various decision tree techniques and compare their performance.
The decision tree techniques used in this study are J48, LMT, Random Forest, Random tree, REPTree,
Decision Stump, and Hoeffding Tree. The analysis proves that Decision Stump provides the highest
accuracy than other techniques
The Relationship Between Quality of Care and Choice of Clinical Computing System: Retrospective Analysis of Family Practice Performance Under the UK Quality and Outcomes Framework
Physician resistance as a barrier to implement clinical information systems b...Healthcare consultant
In summary, physician resistance toward implementation of clinical information systems is a major barrier. Strategies acknowledged under various categories to overwhelm this barrier provide hope to organizations that are eager to take on this adventure. Decision makers carry the chief responsibility to put in advance clear rules to facilitate physicians’ participation in the implementation process to eliminate their later on opposition.
Running Head EVALUATION PLAN FOCUSEVALUATION PLAN FOCUS 1.docxcowinhelen
Running Head: EVALUATION PLAN FOCUS
EVALUATION PLAN FOCUS 1
Evaluation Plan Focus
Student Name
University Affiliations
Date
Professor
Scenario 1:
Your hospital is implementing a new unified acute and ambulatory Electronic Health Record (EHR) system through which patient care documentation will occur. Interdisciplinary assessment forms (including nursing), clinical decision support, and medical notes will be documented in this system. The implementation of the system is anticipated to improve the hospital’s performance in a multitude of areas. In particular, it is hoped that the use of the EHR system will reduce the rate of patient safety events, improve the quality of care, deter sentinel events, reduce patient readmissions, and impact spending. The implementation of the EHR system is also
Introduction
Evaluation plan involves an integral part regarding a grant suggestion providing information aimed at improving a project during the development and implementation. I will participate in the assessment of the scenario system in throughout the project. The scenario includes the hospital that is implementing the new unified as well as the Ambulatory EHR (Electronic Health Record) system that enhances the documentation of patient care. The purpose of the paper is explaining the selected scenario one, explanation of the reasons for selecting it, and summarizing of the research findings on the similar HIT implementations. More so, there is a description of the evaluation viewpoint, and goal guiding the assessment plan and same rationale.
HIT System Selected
The new system to be implemented has various modules that contain interdisciplinary assessment forms, medical notes, and clinical decision support where their documentation is guaranteed. The implementation of the unified system will enhance improved performance of the hospital in several departments. The new EHR system becomes of great importance to the hospital since there is a reduction of medical errors, reduction of the rate of the safety events of each patient, improving the quality of healthcare, deterrence of sentinel events, reduced patients readmissions as well as impact spending. Another reason for choosing the scenario is that the new system will enhance while fulfilling the requirements of meaningful use as stipulated in the HITECH (Health Information Technology for Economic and Clinical Health) Act. Therefore, the need for evaluation regarding the EHR implementation becomes paramount since it will help to identify the associated risks while adjusting the modules required when offering the medication services to the patients (Lanham, Leykum & McDaniel, 2012).
Summary of Research Findings on Similar HIT Implementations
Several evaluations are analogous to the HIT system implementation of the unified system with related differences regarding the outcomes based on the primary goals. For instance, some of the implemented systems fail to meet one hundred percent ...
Recent discovered technologies have exposed many new theories and possibilities to improve our standard of living. Medical assistance has been a major research topic in the past, many efforts were put in to simplify the process of following treatment prescriptions. This paper summarizes the work done in developing LoRa driven medical adherence system in order to improve medicine adherence for elderlies. The designed system is composed of two sections; embedded hardware device for the use of patients at home and Web application to manage all patients along with their medicines and keep track of their medicine intake history. LoRa wireless communication technology is used for connecting all embedded devices with a central gateway that manages the network. Hardware and software tests have been conducted and showed great performance in terms of LoRa network range and latency. In short, the proposed system shows promising method of improving medicine adherence.
Addressing pediatric medication errors in ED setting utilizing Computerized P...Arete-Zoe, LLC
Pediatric patients who are treated in general acute care hospitals are at increased risk of medication errors. The main reasons are the lack of experience with the special needs of pediatric patients, their lower ability to tolerate medication errors, medication-related problems such as forms and packaging designed primarily for adults and labeling with insufficient information on the dosing of pediatric patients. Medication errors can be reduced significantly by appropriate medication management systems. Computerized Provider Order Entry (CPOE) systems reduce the frequency of medication errors in all stages of the process. IT technology introduces an additional vulnerability in the form of IT-related medication errors. Nurses are the last individuals in the medication management process who can detect and intercept a medication error and prevent incorrect medication orders from reaching and harming their patients. To be able to do so, nurses have to be familiar with the medication management system in their hospital and escalate incorrect orders as appropriate and relevant.
I have tried to provide an outline regarding the general antivirals available in our country..and discussed regarding MOA,indications and Therapeutic uses.
2. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
197
introduction of electronic health records
represented change in doctors work flow and
imposed a greater burden on them.[9, 10]
The most
significant barrier to implement CPOE systems is
the physicians do not like to work with. If the
system is developed with the physician‟s
acceptance and collaboration, it would reduce the
resistance to implement CPOE system.
In non-CPOE hospitals, paper-based order sets are
available for clinical use. Implementation of
standardized order sets improves patient‟s safety
[11]
. Comparison between traditional prescription
system, CPOE prescription order with DSS, and
Computerized Order Entry prescription order with
DSS is depicted below in figure 1. Building CPOE
systems require designs that can provide rather
important additional advantages such as safety of
clinical work [12]
. Lee et al [13]
. Created the
Physician Order Entry User Satisfaction and Usage
Survey (POESUS) and used it in a study of 112
physicians and 93 nurses at the Brigham and
Women‟s Hospital (BWH) in Boston. Hoonakker
et al [14]
. used POESUS questionnaire and measured
the CPOE end user satisfaction by studying on 52
participated physicians.
OBJECTIVES
The main objectives of this study are
1. To develop a comprehensive drug database for
CPOE with DSS within the hospital which on
implementation provides information on drug
allergy, appropriate doses, special monitoring
requirement, and drug interactions.
2. To assess the preconceived physician attitude
towards CPOE with DSS by end- user
satisfaction.
METHODS AND MATERIALS
Development of Database: Database is a structured
set of data held in a computer, especially one that is
accessible in various ways. Database Management
System (DBMS) [15]
are designed software
applications that interact with the user, other
applications, and the database itself to capture and
analyze the data. Well known DBMSs include My
SQL, MariaDB, PostgreSQL, SQLite, Microsoft
SQL server, Oracle, SAP HANA, dBASE, FosPro,
IBM DB2, LibreOffice Base and FileMaker Pro.
We have used Microsoft SQL server for the
development of the database entry programme.
Herculean task of comprehensive drug data entry
was made into this system by using subscribed
UPTODATE drug information. CPOE system
usage is depicted in the figure 2
Assessment of End User Satisfaction: End-user
satisfaction is conceptualized as “the affective
attitude towards a specific computer application by
someone who interacts with the application
directly” [16]
. End users are defined as “individuals
who interact directly with the computer”.
Instrument to measure the end user satisfaction:
There are several methods available to measure
end-user satisfaction, such as examining actual use
of computer systems and applications, conducting
interviews with end-users, and using end-user
questionnaires. Using a questionnaire is a relatively
simple method to collect and analyze data. It is
very important to use valid and reliable
questionnaires when doing research, an observation
which may be considered all too obvious.
Data analysis: For the statements the percentage
respondents agreeing (score of 5 on the Likert
scale) was calculated. Participants were shown how
to work with CPOE system and navigate to the
order entry screen, prior to performing their
evaluations. Participants were asked to explore the
various system interfaces and document their
observations and comments as they related to these
principles. The study was carried out in Sri
Ramachandra University, a 1600 bedded tertiary
care teaching hospital. The departments involved in
analyzing the study outcomes are General Surgery,
General Medicine, Reproductive Medicine,
Obstetrics and Gynaecology, Pulmonology,
Pediatrics, Oncology, Emergency Medicine, Oral
maxillofacial surgery and Radiotherapy. The
sample size of the study was 25 questionnaires
filled up the Physicians, and Medical Residents.
Questionnaire instrument: Questionnaires were
developed targeting physicians. The reliability,
ease of use, timeliness, impact on patient safety,
efficacy, and outcome were studied [17]
. Participants
could answer 5 point likert scale method „Agree/
Strongly Agree/ Cannot say/ Disagree/ Strongly
Disagree. In an open-ended question respondents
were asked to mention advantages as well as
disadvantages of the system. The closed end
questions were asked to get the opinions/
comments from the participants. Steps involved in
filling up questionnaire are depicted in the figure 3.
RESULTS
The developed CPOE with DSS was able to
provide information and warnings on appropriate
doses, drug allergy, drug interactions and special
monitoring within the prescribed drugs for a
patient. In our study 25 physicians participated and
84% of participants agreed that CPOE is easy to
use and also felt that risk of medication errors
could be reduced. 68% and 8% of participants
agreed and disagreed respectively that CPOE saves
3. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
198
time. Statement „ability of CPOE to achieve high
level of patient safety‟ was accepted by 84% of the
participants. There was dilemma about CPOE to
cause doubts on the reliability/ completeness of the
data by 40% of participants, while 28% disagreed
to it. All the participants agreed that CPOE
provides potential drug interaction alert. Graphical
representation of responses towards CPOE
questionnaire statements are depicted from figure
4-11. The participants in our study rated the
following statements as positively: achieving high
level of patient safety (mean=4.2, standard
deviation=0.707), saves time (mean=3.88, standard
deviation=0.927), reduce the risk of medication
error (mean=4.36, standard deviation=0.757), and
ease to use (mean=4.28, standard
deviation=0.8426). On the other hand, CPOE
causes doubts about reliability or completeness of
data (mean=3.32, standard deviation=1.107) a
somewhat negative characteristic of CPOE as
perceived by participants.
DISCUSSION
To successfully implement CPOE system, a proper
analysis of the existing system is necessary.
Medication errors are unnoticed in many hospitals
and results in long stay hospitalization. In order to
avoid medication errors and ADE‟s utmost, we
have developed our own CPOE that fits to our
practice. Although commercial software is
available in market, we want to establish our own
CPOE that is acceptable by most of our physicians.
DSS provides the correct dose as a default value [18]
[19]
. However in our CPOE system, DSS provided
information after the physician had made any
mistake in calculating the appropriate dose. Drug
Interactions alert are provided not only with the
current medications but also with the previously
given medications, this enables the physician to
change the prescription accordingly. Drug allergy
alert provides the physician not to enter the same
drug during hospital stay and also after discharging
the patient. Our CPOE system was able to display
warnings for drugs that require special monitoring.
This enables the physician to adjust the dose
accordingly. CPOE is widely used in developed
countries. For determining our physician‟s attitude
towards CPOE, we compared our results with study
done by Hoonakker et al. and is depicted in table 1.
Our study focused on the measurement of
characteristics like reliability, ease of use,
timeliness, impact on patient safety and outcome.
Our questionnaire contains 21 statements, out of
which 4 statements are demographics of
participant, 14 statements were open ended
questions and 3 statements were close ended
questions. Out of 14 statements, 5 statements were
selected which are practical, significant and
resemble to the POESUS questionnaires and are
studied. Most of our questionnaires are filled by
physicians who had clinical experience of greater
than 8years. The mean of the selected items was
found to be 4.108 while the Hoonakker et al. study
had a mean of 4.06. Results of our study show
significantly higher scores on the different aspects
of user satisfaction. The standard deviation of this
study was comparatively less when compared to
Hoonakker et al. study. Due to the existence of
CPOE from many years in the latter study, there
was more reliability on the data entry by
prescribers. We found statistically difference on the
CPOE characteristic viz. doubts about reliability/
completeness of data (mean=3.32) than with
Hoonakker et al. study. While other CPOE
characteristics of this study were significant with
the Hoonakker et al. study.
CONCLUSION
CPOE with DSS systems are being increasingly
implemented in hospitals and other healthcare
settings. 88% of participants, agreed to the use of
CPOE with DSS in our hospital setting. Our
physician‟s attitude towards CPOE with DSS was a
positive outcome to implement the same in our
hospital and frequently update the drug
information, and there is a need to utilize different
physician‟s comments to bring up an effective
Clinical Decision Support System. End- User
Satisfaction study should be retested periodically to
know the change in outcomes with respect to the
current study. Hospitals that implement this
technology need to evaluate the impact of the
CPOE technology on end users in order to identify
problems with implementations and to plan
continuing optimization initiatives. CPOE with
DSS aids the physician and doesn‟t replace the
physician authority.
ACKNOWLEDGEMENT
We sincerely extend our thanks to Mr. Subash, Ms.
Gayatri, Ms. Saranya for technical assistance. We
express our heartfelt thanks to Dr. Anusha for
encouragement and assisting in database archiving
and publication process without whom we
wouldn‟t have reached destination. We would like
to acknowledge with deep sense of gratitude to all
our friends for their support.
4. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
199
Figure 1: Comparison between Traditional medication order entry, CPOE and
Computerized Order Entry (COE)
Traditional medication order entry CPOE COE
5. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
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Figure 2: CPOE system usage
YES
YES
NO
YES
NO
PATIENT ID
PATIENTS
Is any Drug
allergic to the
Patient?
ALLERGIC
DRUGS ENTRY
EXCLUSION OF
ALLERGIC
DRUGS
DRUG SELECTION
PRESCRIBED DOSE
AND DAILY DOSE
CALCULATION
WARNING
DISPLAY
Does Prescribed
dose exceed
maximum Dose?
NO
WARNING
DISPLAY
Does any drug interact
with other, special
monitoring required?
PRESCRIPTION SUBMISSION
6. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
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YES
NO
SECOND TIME
ENTRY FOR
SAME PATIENT
SECOND TIME
ENTRY FOR SAME
PATIENT
PREVIOUS
PRESCRIPTION
VIEW
Does any Previous
drug interact with
the current drugs?
DISPLAY
WARNING
PRESCRIPTION
SUBMISSION
7. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
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Figure 3: Steps involved in filling up questionnaire
Figure 4:
8. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
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Figure 5:
Figure 6:
9. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
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Figure 7:
Figure 8:
10. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
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Figure 9:
Figure 10:
11. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
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Figure 11:
Table 1: Comparison of our study results with Hoonaker et al.
Items Our study Hoonakker at al
Order entry is easy to use within
the routine work
Mean: 4.28
SD: 0.8426
[N = 25]
Mean: 4.22
SD: 1.54
[N = 51]
Order entry reduces the risk of
medication errors
Mean: 4.36
SD: 0.757
[N = 25]
Mean: 4.53
SD: 1.16
[N = 52]
Order entry saves time for staff
Mean: 3.88
SD: 0.927
[N = 25]
Mean: 4.21
SD: 1.71
[N = 52]
Order entry helps to achieve a high
level of patient safety
Mean: 4.2
SD: 0.707
[N = 25]
Mean: 4.67
SD: 1.14
[N = 51]
Order entry cause doubts about
reliability / completeness of data
Mean: 3.32
SD: 1.107
[N = 25]
Mean: 5.94
SD: 1.0
[N = 52]
SD- Standard Deviation, N- Total number of participants
12. Hareesh et al., World J Pharm Sci 2015; 3(2): 196-207
207
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