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Fatigue Determines Work Motivation Riska Rosita; Rahaju Muljo Wulandari; Pamela Hani Maretesia Putri; Sri Atun Amba Wani
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 1st International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

The hospital officer potentially experience fatigue caused by work, especially the medical records staff. Many of work less ergonomics, monotonous, and less rest time impact on work motivation. So needed research aimed at work know the effect of fatigue on work motivation on medical record officers at Dr. Arif Zainudin Hospital. This quantitative research uses the correlation method with cross sectional approach, population all medical records officers in Dr. Arif Zainudin Hospital. Research result show that age categories characteristics of respondents in the most 31-39 as much 41%, temporary employee status as much 54%, the highest level of education is diploma as much 59%, fatigue rates at medium level is 50%, and work motivation rates at a high level is 68%. Through fisher exact test shows that results p = 0.00 in significance 5%. The conclusion of this research there is a significant relationship between fatigue and work motivation on medical record officers at Dr. Arif Zainudin Hospital. Fatigue determines work motivation on medical record officers. The researcher's suggestion is that the hospital staffing department should redesign the work station, make variations in the work so that it is not static or monotonous, and provide sufficient rest time for the staff, so as to reduce the impact of work fatigue and increase the work motivation of the officers.

The Workload of Health Workers in The Medical Record Unit During the Covid-19 Pandemic Riska Rosita; Devi Prasetyo Ramadani; Diyan Nurhaini; Ratih Kusumaningtyas; Indra Agung Yudistiro; Harjanti
Proceeding of International Conference on Science, Health, And Technology 2021: Proceeding of the 2nd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

The medical record unit is the holder of data for all patients so that the performance of medical record workers supports the quality of health services. The problem that occurs in the Community Health centers at Sawit Indonesia, Medical Record Unit is the limited number of workers so that the service to patients tends to belong. The number of patient visits during the Covid-19 pandemic reached 80-100 people/day. The purpose of this study was to analyze the workload of medical record unit workers. The research method used is qualitative with a descriptive research design. Data collection was carried out using observation and interviews. The results showed that: (1) registration workers were carried out by nurses, who served patient registration as well as assisting doctors in providing medication to patients and operating ambulances when needed, as a result, workers experienced work fatigue; (2) analyzing-reporting workers concurrently serve as cashiers, resulting in human errors, inaccurate data and inability to finish on time; and (3) filing workers are carried out by midwives who also provide medical treatment to patients so that the provision of medical record documents is delayed. Workers complain about fatigue, feeling dizzy, drowsy at work, loss of concentration, etc. Researchers suggest that Community Health centers at Sawit district, prepare job descriptions for workers so that the medical record unit can be carried out according to its function.

APPLICATIONS TO MONITOR MATERNAL HEALTH IN PREVENTING STUNTING Riska Rosita; Tominanto Tominanto; Andi Yulianto
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 3rd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Adequate nutrition during pregnancy affects the incidence of stunting. Every pregnant woman is encouraged to have regular prenatal checkups. But many pregnant women and nursing mothers do not routinely do checkups. They even lost a book to monitor Maternal and Child Health, so the examination history was not fully documented. This creates problems such as incomplete patient data, and midwives having difficulty finding patient history. The purpose of this study is to develop an application to monitor maternal health. Monitoring is also carried out to help detect early if there are potential abnormalities or health problems for the mother and fetus. The research method uses the Rapid Application Development (RAD) approach, by developing computer applications to monitor maternal health. The results show that this application can produce information on the history of physical examinations on patients more quickly and accurately. Reports are presented in the form of printouts and graphics. The graphic display makes it easier for the midwife to read the progress of the patient's health. Midwives are also faster in detecting abnormal patient conditions, so midwives can immediately provide education, action, or therapy.

PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.

Quality Evaluation on The Implementation of Electronic Medical Records in Primary Health Centers Rosita, Riska; Wisda Tumarta Arif, Yunita; Rohman Wachid, Naufal; Ristianingsih, Tsasa
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4171

Primary Health Centers in Indonesia must implement Electronic Medical Records (EMR) through SIMPUS in accordance with PMK/24/2022 concerning Medical Records. Primary Health Center X has implemented EMR since September 2023. However, EMR often experiences downtime, which hampers the process of distributing medical records between health service units and patients must wait until the server returns to normal. This study aims to conduct an evaluation to determine the quality of EMR at Primary Health Center X. This type of research is a qualitative descriptive study with a cross-sectional approach. The population were all health workers in the outpatient service section at Primary Health Center X, with a sample taken using the total sampling technique of 19 respondents. Data analysis was carried out descriptively quantitatively on the EMR system using the PIECES method. The results of the EMR quality evaluation study based on the PIECES method show that the performance aspect is categorized as good (72.9%), the information aspect is categorized as very good (76.5%), the economy aspect is categorized as very good (79.3%), the control aspect is categorized as very good (75.1%), the efficiency aspect is categorized as very good (76.6%) and the service aspect is categorized as very good (76.0%). The conclusion of the calculation of the average percentage index of all aspects is that the EMR system is very good (76.06%).

PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.

Quality Evaluation on The Implementation of Electronic Medical Records in Primary Health Centers Rosita, Riska; Wisda Tumarta Arif, Yunita; Rohman Wachid, Naufal; Ristianingsih, Tsasa
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4171

Primary Health Centers in Indonesia must implement Electronic Medical Records (EMR) through SIMPUS in accordance with PMK/24/2022 concerning Medical Records. Primary Health Center X has implemented EMR since September 2023. However, EMR often experiences downtime, which hampers the process of distributing medical records between health service units and patients must wait until the server returns to normal. This study aims to conduct an evaluation to determine the quality of EMR at Primary Health Center X. This type of research is a qualitative descriptive study with a cross-sectional approach. The population were all health workers in the outpatient service section at Primary Health Center X, with a sample taken using the total sampling technique of 19 respondents. Data analysis was carried out descriptively quantitatively on the EMR system using the PIECES method. The results of the EMR quality evaluation study based on the PIECES method show that the performance aspect is categorized as good (72.9%), the information aspect is categorized as very good (76.5%), the economy aspect is categorized as very good (79.3%), the control aspect is categorized as very good (75.1%), the efficiency aspect is categorized as very good (76.6%) and the service aspect is categorized as very good (76.0%). The conclusion of the calculation of the average percentage index of all aspects is that the EMR system is very good (76.06%).

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