Developing hospital resilience domains in facing disruption era in Indonesia: a qualitative study.

BACKGROUND: The studies of hospital resilience have been of increasing importance during the last decade due to disasters and pandemics. However, studies in developing the domain and indicators of hospital resilience were limited mainly on disaster response. A few studies of hospital resilience focused on how to deal with disruptions such as environmental turbulence, rapid technological changes, and changes in patient preferences. This study aims to develop domains and indicators of hospital resilience in facing the disruption era. METHODS: This qualitative study focused on exploring the domains and indicators to face disruptions that have been identified in the first exploratory phase of the studies. Key informants included hospital experts from the government, medical practitioners, and academics. A total of 20 key informants were involved in semi-structured interviews which were conducted face-to-face, via telephone and Zoom. Data was analyzed using a grounded theory approach to discover domains for a resilient hospital. RESULTS: The study identified a number of domains that are fundamental for a hospital to become a resilient in the face of disruption. These include readiness to face digital transformation, effective leadership, and flexibility in managing resources among others. Situation awareness and resilience ethos, implementation of marketing management, networking, and disaster anticipation are found to be equally important. These domains focused on the hospital's ability to deal with specific shocks from different perspectives as the result of changes from disruptions which are inevitable within the organizational business environment. CONCLUSIONS: The domains identified in the study are able to respond to the limitations of the concept of hospital resilience, which is currently more focused on hospital disaster resilience. They can be used to measure hospital resilience in the context of the volatility, uncertainty, complexity, and ambiguity (VUCA), which are relevant to the context of the Indonesia hospital industry.

How to detect healthcare fraud? "A systematic review".

OBJECTIVE: To identify the method used in detecting fraud cases. METHODS: Articles searching by using topic-appropriate keywords and incorporated into search engines (data-based) journals Pubmed/Medline, Cochrane, Wiley, ScienceDirect, and secondary data-based Google scholar. Then data extraction is done based on inclusion criteria. The selected articles have the aim of investigating/detecting cases of fraud that have occurred in the health sector or other related sectors that support the study. RESULTS: The findings of the nine reviewed articles have suggested that most of the fraud perpetrators are performed by medical personnel (doctors) and providers. Many types of fraud occur such as insurance claims or medical actions that are completely unadministered nor following the procedure and duplicating claims. The methods that appropriate to be used in detecting fraud are secondary data tracking, information, and technology specialist provision. CONCLUSION: Secondary data tracking is the most widely used method in fraud detection. Fraud perpetrators are ones who dominated by medical circles with fictitious claim cases. Perpetrators tend not to act themselves but in organizations with network.

How is the correlation job crafting to job satisfaction of hospital staff at disruption era in hospital industries.

OBJECTIVE: This study aimed to see the correlation between JC and JS of hospital staff in Rural area, Mamuju at 4.0 era. METHOD: The population was all staff of three hospitals, 614 respectively at Mamuju regency, West Sulawesi Province and 155 staff selected as samples. The sample was chosen by random quota sampling, then analyzed using the Spearman correlation test. RESULTS: The results showed that most of the staff were satisfied with their job (90% average) while more than 80% of staff perceived the did JC. The result of the correlation test revealed that the p value of 0.005<0.05 means hypothesis null was rejected. There was correlation between JC and JS. The correlation coefficient was 0.224 showing a positive direction of the relationship even though it was a weak correlation. CONCLUSIONS: JC can cause JS when the staff have other supporting factors. JC may enhance JS significantly when supported by other factors, such as job security, good compensation work itself and relationship with immediate supervisor.

How is the correlation job crafting to job satisfaction of hospital staff at disruption era in hospital industries

Objective: This study aimed to see the correlation between JC and JS of hospital staff in Rural area, Mamuju at 4.0 era. Method: The population was all staff of three hospitals, 614 respectively at Mamuju regency, West Sulawesi Province and 155 staff selected as samples. The sample was chosen by random quota sampling, then analyzed using the Spearman correlation test. Results: The results showed that most of the staff were satisfied with their job (90% average) while more than 80% of staff perceived the did JC. The result of the correlation test revealed that the p value of 0.005 < 0.05 means hypothesis null was rejected. There was correlation between JC and JS. The correlation coefficient was 0.224 showing a positive direction of the relationship even though it was a weak correlation. Conclusions: JC can cause JS when the staff have other supporting factors. JC may enhance JS significantly when supported by other factors, such as job security, good compensation work itself and relationship with immediate supervisor. (AU)

How to detect healthcare fraud? “A systematic review”

Objective: To identify the method used in detecting fraud cases. Methods: Articles searching by using topic-appropriate keywords and incorporated into search engines (data-based) journals Pubmed/Medline, Cochrane, Wiley, ScienceDirect, and secondary data-based Google scholar. Then data extraction is done based on inclusion criteria. The selected articles have the aim of investigating/detecting cases of fraud that have occurred in the health sector or other related sectors that support the study. Results: The findings of the nine reviewed articles have suggested that most of the fraud perpetrators are performed by medical personnel (doctors) and providers. Many types of fraud occur such as insurance claims or medical actions that are completely unadministered nor following the procedure and duplicating claims. The methods that appropriate to be used in detecting fraud are secondary data tracking, information, and technology specialist provision. Conclusion: Secondary data tracking is the most widely used method in fraud detection. Fraud perpetrators are ones who dominated by medical circles with fictitious claim cases. Perpetrators tend not to act themselves but in organizations with network. (AU)