scholarly journals Implementation of the electronic medical record system in the radiation oncology department of a government health-care facility: A single-center experience

2020 ◽  
Vol 3 (4) ◽  
pp. 748
Author(s):  
RA Sunil ◽  
Lokesh Vishwanath ◽  
T Naveen ◽  
Siddanna Pallad ◽  
SanjeetKumar Mandal ◽  
...  
Keyword(s):  
Health Care ◽  
Medical Record ◽  
Radiation Oncology ◽  
Care Facility ◽  
Health Care Facility ◽  
Electronic Medical Record System ◽  
Single Center ◽  
Record System ◽  
Single Center Experience ◽  
Medical Record System

scholarly journals Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study

10.2196/24490 ◽  
2020 ◽  
Vol 4 (12) ◽  
pp. e24490
Author(s):  
Gumpili Sai Prashanthi ◽  
Ayush Deva ◽  
Ranganath Vadapalli ◽  
Anthony Vipin Das
Keyword(s):  
Health Care ◽  
Positive Predictive Value ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Finite State Machine ◽  
Electronic Medical Record System ◽  
State Machine ◽  
Record System ◽  
Medical Record System ◽  
Finite State

Background One of the major challenges in the health care sector is that approximately 80% of generated data remains unstructured and unused. Since it is difficult to handle unstructured data from electronic medical record systems, it tends to be neglected for analyses in most hospitals and medical centers. Therefore, there is a need to analyze unstructured big data in health care systems so that we can optimally utilize and unearth all unexploited information from it. Objective In this study, we aimed to extract a list of diseases and associated keywords along with the corresponding time durations from an indigenously developed electronic medical record system and describe the possibility of analytics from the acquired datasets. Methods We propose a novel, finite-state machine to sequentially detect and cluster disease names from patients’ medical history. We defined 3 states in the finite-state machine and transition matrix, which depend on the identified keyword. In addition, we also defined a state-change action matrix, which is essentially an action associated with each transition. The dataset used in this study was obtained from an indigenously developed electronic medical record system called eyeSmart that was implemented across a large, multitier ophthalmology network in India. The dataset included patients’ past medical history and contained records of 10,000 distinct patients. Results We extracted disease names and associated keywords by using the finite-state machine with an accuracy of 95%, sensitivity of 94.9%, and positive predictive value of 100%. For the extraction of the duration of disease, the machine’s accuracy was 93%, sensitivity was 92.9%, and the positive predictive value was 100%. Conclusions We demonstrated that the finite-state machine we developed in this study can be used to accurately identify disease names, associated keywords, and time durations from a large cohort of patient records obtained using an electronic medical record system.

scholarly journals Use of more than one electronic medical record system within a single health care organization

2012 ◽  
Vol 03 (04) ◽  
pp. 462-474 ◽  
Author(s):  
J. Fellner ◽  
C. Dugowson ◽  
D. Liebovitz ◽  
G. Fletcher ◽  
T. Payne
Keyword(s):  
Health Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Health Care Organization ◽  
Electronic Medical Record System ◽  
Healthcare Organization ◽  
Care Organization ◽  
Data Types ◽  
Record System ◽  
Medical Record System

SummaryHealthcare organizations vary in the number of electronic medical record (EMR) systems they use. Some use a single EMR for nearly all care they provide, while others use EMRs from more than one vendor. These strategies create a mixture of advantages, risks and costs. Based on our experience in two organizations over a decade, we analyzed use of more than one EMR within our two health care organizations to identify advantages, risks and costs that use of more than one EMR presents. We identified the data and functionality types that pose the greatest challenge to patient safety and efficiency. We present a model to classify patterns of use of more than one EMR within a single healthcare organization, and identified the most important 28 data types and 4 areas of functionality that in our experience present special challenges and safety risks with use of more than one EMR within a single healthcare organization. The use of more than one EMR in a single organization may be the chosen approach for many reasons, but in our organizations the limitations of this approach have also become clear. Those who use and support EMRs realize that to safely and efficiently use more than one EMR, a considerable amount of IT work is necessary. Thorough understanding of the challenges in using more than one EMR is an important prerequisite to minimizing the risks of using more than one EMR to care for patients in a single healthcare organization. Citation: Payne T, Fellner J, Dugowson C, Liebovitz D, Fletcher G. Use of more than one electronic medical record system within a single health care organization. Appl Clin Inf 2012; 3: 462–474http://dx.doi.org/10.4338/ACI-2012-10-RA-0040

scholarly journals Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study (Preprint)

2020 ◽  
Author(s):  
Gumpili Sai Prashanthi ◽  
Ayush Deva ◽  
Ranganath Vadapalli ◽  
Anthony Vipin Das
Keyword(s):  
Health Care ◽  
Positive Predictive Value ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Finite State Machine ◽  
Electronic Medical Record System ◽  
State Machine ◽  
Record System ◽  
Medical Record System ◽  
Finite State

BACKGROUND One of the major challenges in the health care sector is that approximately 80% of generated data remains unstructured and unused. Since it is difficult to handle unstructured data from electronic medical record systems, it tends to be neglected for analyses in most hospitals and medical centers. Therefore, there is a need to analyze unstructured big data in health care systems so that we can optimally utilize and unearth all unexploited information from it. OBJECTIVE In this study, we aimed to extract a list of diseases and associated keywords along with the corresponding time durations from an indigenously developed electronic medical record system and describe the possibility of analytics from the acquired datasets. METHODS We propose a novel, finite-state machine to sequentially detect and cluster disease names from patients’ medical history. We defined 3 states in the finite-state machine and transition matrix, which depend on the identified keyword. In addition, we also defined a state-change action matrix, which is essentially an action associated with each transition. The dataset used in this study was obtained from an indigenously developed electronic medical record system called eyeSmart that was implemented across a large, multitier ophthalmology network in India. The dataset included patients’ past medical history and contained records of 10,000 distinct patients. RESULTS We extracted disease names and associated keywords by using the finite-state machine with an accuracy of 95%, sensitivity of 94.9%, and positive predictive value of 100%. For the extraction of the duration of disease, the machine’s accuracy was 93%, sensitivity was 92.9%, and the positive predictive value was 100%. CONCLUSIONS We demonstrated that the finite-state machine we developed in this study can be used to accurately identify disease names, associated keywords, and time durations from a large cohort of patient records obtained using an electronic medical record system.

scholarly journals Exploring patients satisfaction after the implementation of an electronic medical record system at Al-Wakrah primary health center, Qatar, 2016

2017 ◽  
Vol 4 (10) ◽  
pp. 3511 ◽  
Author(s):  
Ayman F. Al-Dahshan ◽  
Noura Al-Kubaisi ◽  
Mohamed Abdel Halim Chehab ◽  
Nour Al-Hanafi
Keyword(s):  
Health Care ◽  
Patient Satisfaction ◽  
Primary Health Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Electronic Medical Record System ◽  
Record System ◽  
Health Care Center ◽  
Primary Health ◽  
Medical Record System

Background: The healthcare industry has focused much attention on patient satisfaction with the quality of healthcare services. However, there remains a lack of research on patient satisfaction towards the implementation of an electronic medical record system at a primary healthcare setting. This study aimed at assessing the level of patient satisfaction regarding primary health care services after the implementation of an electronic medical record (EMR) system. Methods: A descriptive cross-sectional study was conducted at the Al-Wakrah health care center, with a random/convenient sample of 52 patients attending the center. Furthermore, the investigators interviewed the participants, in the waiting area, regarding their satisfaction with the primary health care services provided following the EMR system implementation. A structured interview-based questionnaire for measuring patient satisfaction was employed. Results: The vast majority of participants indicated that the overall service at the health center greatly improved after EMR implementation. Furthermore, most interviewees were totally satisfied with the overall workflow at the health care center such as the time spent at the registration desk (76.9%), before seeing a physician (65.4%), while the physician used the computer (76.9%), physical examination (69.3%), laboratory testing (73.1%), and collecting the medication (65.4%). Regarding health education and informativeness, the participants found that labeling medication bottles was quite informative. However, less than two-thirds (61.5%) of the patients were satisfied with the health education delivered by physicians. Conclusions: The results revealed that although overall patient satisfaction was relatively high, certain aspects of the health care service remained to be a source of dissatisfaction. Thus, this study demonstrated patient acceptance and support for the electronic medical record system at the primary health care setting. 

The Great Awakening - Digitalization in Dental Healthcare during the pandemic COVID-19

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 380-384
Author(s):  
Priyanka Paul Madhu ◽  
Yojana Patil ◽  
Aishwarya Rajesh Shinde ◽  
Sangeeta Kumar ◽  
Pratik Phansopkar
Keyword(s):  
Health Care ◽  
Task Force ◽  
Care Service ◽  
Health Care Service ◽  
Care Facility ◽  
Health Care Facility ◽  
Health Care Facilities ◽  
World Health ◽  
Record System ◽  
The World

disease in 2019, also called COVID-19, which has been widely spread worldwide had given rise to a pandemic situation. The public health emergency of international concern declared the agent as the (SARS-CoV-2) the severe acute respiratory syndrome and the World Health Organization had activated significant surveillance to prevent the spread of this infection across the world. Taking into the account about the rigorousness of COVID-19, and in the spark of the enormous dedication of several dental associations, it is essential to be enlightened with the recommendations to supervise dental patients and prevent any of education to the dental graduates due to institutional closure. One of the approaching expertise that combines technology, communications and health care facilities are to refine patient care, it’s at the cutting edge of the present technological switch in medicine and applied sciences. Dentistry has been improved by cloud technology which has refined and implemented various methods to upgrade electronic health record system, educational projects, social network and patient communication. Technology has immensely saved the world. Economically and has created an institutional task force to uplift the health care service during the COVID 19 pandemic crisis. Hence, the pandemic has struck an awakening of the practice of informatics in a health care facility which should be implemented and updated at the highest priority.

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