BACKGROUND STUDIES /PROBLEM STATEMENT:
Recent maintenance practices in hospital (health care equipments and services) and use of computerized maintenance management system (CMMs) based hospital information maintenance management system (HIMMs) provide good practices during evaluating performance and effectiveness of equipments. But unfortunately, CMMs and maintenance activities i.e. Breakdown maintenance (BDM), planned preventive maintenance (PPM), productivity is not solve problems at all. Several reasons are: 1. CMMs not solve problems of repetitive failures, BDM , PPM for long term 2. CMMs also not predict life cycle and durability of equipments for long period For this reason, need of such system that can predict and resolve problems at all.
This research project is aimed to provide two ways to solve problems by using theoretically mathematical equations from literature review and modify equations for particular for repetitive failure, BDM, PPM and equipment productivity. Second way CMMs software is to design which is completely reliable; predictable critical factors for not only now, but also long term purpose. The results in terms of system assist facility managers or clinical engineers (biomedical engineers). Sustain life of equipments, easy to replacement and repaired etc.
OBJECTIVES OF RESEARCH PROJECT:
The objectives of the project are: 1) To evaluate maintenance management system in hospital (health care equipments and services) 2) To modify mathematical equations for measuring maintenance activities of ultrasound equipment and CT scan equipment
METHODOLOGY: Data Collection Strategy: Data collection strategy is obtained from visited and observation biomedical engineering department. Select two imaging equipments -ultrasound machine’ & -computed tomography machine’ from various hospitals like Hospital Serdang, Hospital Putrajaya etc., to collect equipments inventory data. Data collection strategy is focus equipment assets history. It includes planned preventive maintenance (PPM), Breakdown maintenance (BDM), Schedule work, work orders (w.o), action taken, vendor services, parts management, utilities, and reports. Data collection is need because: a) Calculate repetitive failures, BDM and PPM b) Measure Productivity and Equipment effectiveness c) Life and use of equipments for long term purpose Analysis of Data Collection: Analysis of the data depends upon asset history of particular equipments. Identify repetitive breakdowns, planned preventive and productivity performance in both medical equipments. The strategy for analyze data is to specified no. of failures, no. of breakdowns and no. of planned preventive for last three years. Analysis of data collection is required because: a) Interpret particular problems b) Apply modify equations to solve problems Mathematical Analysis In general, previous studies on maintenance management system provided mathematical formulas and equations not at all overcome problems of equipments. In medical equipments i.e. reliability equations and availability equations are not enough to measure maintenance performance of ultrasound and CT scan. Predicted Breakdowns
In this project, provide specific and modify equations which is use to solve medical equipments maintenenace problems. (Stephen, 2004) This equations are modified on the basis of productivity and reliability maintenance management.. Stephen defined equations: Breakdown (Excepted)= S (No. of Failures) * Frequency of Failure / Total No. of Failures, But modify equation will be: Breakdowns (predictable) = (a1+a2+a3—-an) * () / Tf Therefore a is no. of repetitive failures, an is nth term of failure which is expecting failure with time, is frequency of failure occurring in duration week, months, Tf is total no. of failures per years. Conclusion In the end, evaluating MMS in hospital is prime consideration for clinical engineers. This modify equations of equipments will improve maintenance practices overall and overcome failures, breakdowns. Successful in implementing and applying mathematical equations will definitely increases productivity and overall equipment effectiveness. Nonetheless, due to the necessity to ensure maintenance management system, figure 4.9 data analyzed and interpreted from maintenance part of the asset of equipments contains several factors that serve as the determinants to the success of maintenance practice for health care sectors. By using mathematical approaches and data analysis from maintenance part of the asset of equipments contains several factors that serve as the determinants to the success of effectiveness of equipment and best maintenance practice in health care industries.
RESULTS: 1. From above calculation, the result shows that repetitive failures, BDM, PPM always key parts and critical factors in maintenance practices.
2. Breakdowns maintenance in ultrasound much more and predictable during last three year so it would be a greater expensive alternatives if equipments not replaced on time. 3. CT scan has low rate of breakdowns maintenance during last three years so it would be predicted that less equipment expensive alternative so no need to replace equipment.
4. Medical Equipment’s productivity factor in ultrasound is 36 % in last three years due to more and more Break downs maintenance and Planned preventive maintenance occurred which is not good for productivity, life of equipment. Step should be taken in early stage of breakdowns. 5. Productivity factor in CT scan is 38 % previous three years, because of less BDM but more PPM. 6. Overall equipment effectiveness of Ultrasound is 38.90, it means equipment is in poor condition and unlimited break downs, more failures occurred. Require proper testing, calibrations, changeovers parts, adjustments. It will contribute to increase in effectiveness in rarely manner. 7. OEE of CT scan is 72 %, it is mean that equipment is in good condition because of breakdowns and failures are limited. In occasionally testing and commissioning, inspection is requiring. It will mostly increase effectiveness in rapid manner.
8. It will increase productivity & It will maintain overall equipment effectiveness.
9. Lastly, it will assist clinical engineers to make decision on need basis of particular equipment or replacement.
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