**5. Occupational Personal Health Systems (O-PHS)**

Most developed countries include within their basic welfare policies the right of citiziens, as workers, to be protected from sickness, disease and injury arising from their employment. Despite this intention, the International Labour Organization (ILO) estimates that 160 million workers are victims of occupational accidents and diseases every year [14] and over two million of people lose their lives from work-related accidents and diseases. The standards on occupational safety and health provide necessary tools for governments, employers, and workers to establish such practices and to provide for full safety at work. In 2003, ILO assumed a global strategy to improve occupational safety and health, which included the introduction of a preventive safety and health culture, the promotion and development of relevant instruments, and technical assistance [14].

One of the European objectives set for 2020 is the 25% reduction in the number of industrial accidents [15][16]. In order to reduce accidents it is essential to pay attention to the workers, their single workplaces and to their working conditions. In addition, favourable environments make workers feel more comfortable while they are in the factories, and thus the efficiency is increased. As a consequence, it is possible to obtain the maximum efficiency in the factory as a whole, which also produces economic benefit for the company. From a healthcare point of view, factories lack normally in an amount of enough information to allow a holistic care of the worker. Health data stored by companies are only a small amount of data, usually stored once a year, and referred to the physical condition of a person just in a particular moment [17].

### **5.1. Proactive paradigm for Occupational Health Systems**

For these reason, future factories and enterprises need to do an effort in focusing resources and strategic planning towards making the workplace safer, healthier and to significantly reduce the number of accidents and the work related diseases in their population. In order

for this to happen, it's necessary to anticipate and predict the occurrence of risk scenarios that can lead to a damaging situation, either by accident or health threat. This need generates a change of paradigm, moving from a reactive system providing management solutions to problems that have already happened and basic preventive measures, to a much more proactive model, where risk management is understood as a mostly preventive tool. To achieve this, it's important to collect, measure and analyze data during a continuous period of time, in order to evaluate the risks and their evolution.

Integrated and Personalised Risk Management in the Sensing Enterprise 301

and "human host", even capable of altering it and causing a potential disease situation where such agents exceed the capabilities of the host response and adaptation. This allows to establish, in accordance with the main risk factors (physical, chemical, biological, social and psychological), a pre pathogenic state or period, which we have called susceptibility stage, on which a primary prevention action is required, and other state or pathogenic period, which we called the disease, which holds the secondary and tertiary preventive

From here, setting up standard profiles (comparable to health profiles), and pathogenic and pre-pathogenic profiles with abnormality parameters, becomes much more simple and affordable and allows to introduce these profiles in the system. The development and implementation of a comprehensive and interrelated system of identification and control of the elements participating in the working environment through sensory and monitoring systems, can establish and develop what we have called the SATSE or System to Aid Decision Making in Occupational Medicine. This system consists of multiple modules that collect data and information about the company, the job (Identification of potential risks, protocols to be applied, pathobiological profiles to be determined, potential limitations, referrals ...), the worker (background, demographics and psychosocial factors, medical history at work,...), extra-clinical data (organizational profile, demographic profile, psychological profile...) and additional data (diagnostic algorithms, performance algorithms, medical knowledge data bases...) The use of physiological sensors that help us to determine both in the workplace and outside it, the physiological status or health of a worker at a given time at work or outside work (whenever necessary), complements the sensor system

Health data has been traditionally produced and owned by the Healthcare Systems and stored in Electronic Healthcare Records (EHR), focusing mainly in describing clinical procedures, tests and values. However, with the purpose of improving the characterization of the person and his environment, EHR data needs to be extended. This new information is stored, together with the EHR information, in other repositories. These repositories are known as PHR (Personal Health Record) [19] and collect data such as habits, preferences, information about the family, work, moods or nutritional profile. These repositories are, in opposition to the EHR, owned by the person, who has the option to share it with whoever

The usage of PHRs in occupational health could enable that when an employee goes to work in a company for the first time, the enterprise's health professionals can ask him to share his relevant PHR data, in order to have his personal file more complete and enable a more complete and accurate health risk management. Of course, this situation would require enhanced methods for privacy and data protection, ensuring no unauthorized and adequate usage of the health information is made. In general, current PHRs contain a summarized version of the EHR adapted to the patient's knowledge and needs and, in some cases, home

actions and care actions where necessary.

module, that is one of the basic elements of the project.

**5.2. Personal Health Records (PHR)** 

he chooses.

In this line, future enterprise risk management solution should turn punctual monitoring into a more frequent and personalized vigilance, including individual and collective data. However, collecting information of many people during a long period of time requires collecting a big amount of data. People are not able to process so much information, so intelligent systems for massive data processing are needed. These intelligent systems classify data and generate alarms associated to the worker. Thanks to these alerts and all the other environmental and personal data stored, it is possible to predict health threats. Thus, it is possible to act in the most appropriate way for each worker in particular.

Nowadays, the number of sensors for monitoring personal health data is increasing. In addition, sensors that collect environmental parameters in industrial factories are being introduced more and more. The problem encountered so far, besides a reduced frequency of monitoring, is that these data are usually not thoroughly connected. The information is only collected in order to produce isolated diagnosis or identify single risks, but not common results, and the collected data become less relevant if they are not treated together. The final decision, in a dynamic environment like a factory, could be more precise if results came from a comprehensive study of a diverse set of parameters. However, once the data is collected and stored, a significant effort must be done in processing it in order to identify and highlight those elements that are directly related to present or future risks. This identification becomes more and more difficult as the amount of data analyzed increases and rage of risks augments. Therefore, it seems logical to state that, in parallel to the data collection efforts, new solutions for clustering, prioritizing and filtering information need to be put in place, to generate the most appropriate alarms in the right moment and in the right place. Risks nature can vary from emergency situations to predictive probabilities and Risk Management systems have to be able to discriminate between the two (and the whole range in between) and provide adequate communication of the contextual information so that the reaction to the risk matches the risk characteristics. Finally, once the information has been monitored and classified, the next point is focused on the intervention. With the aim of representing prevention protocols for this intervention, workflows are developed. Given the workers singularity, the adaptation of the prevention protocols is needed for each one of them. In this way, the elimination of the occupational hazard is much more effective

The Health model established in FASyS, is based on the "Ecological Concept of Disease", in which, the environment (Physical, Social, Economic and Biology, among others) is a set of external conditions and influences affecting the life and development of an organism, human behaviour or society, acting on the balance between the so-called "disease agents" and "human host", even capable of altering it and causing a potential disease situation where such agents exceed the capabilities of the host response and adaptation. This allows to establish, in accordance with the main risk factors (physical, chemical, biological, social and psychological), a pre pathogenic state or period, which we have called susceptibility stage, on which a primary prevention action is required, and other state or pathogenic period, which we called the disease, which holds the secondary and tertiary preventive actions and care actions where necessary.

From here, setting up standard profiles (comparable to health profiles), and pathogenic and pre-pathogenic profiles with abnormality parameters, becomes much more simple and affordable and allows to introduce these profiles in the system. The development and implementation of a comprehensive and interrelated system of identification and control of the elements participating in the working environment through sensory and monitoring systems, can establish and develop what we have called the SATSE or System to Aid Decision Making in Occupational Medicine. This system consists of multiple modules that collect data and information about the company, the job (Identification of potential risks, protocols to be applied, pathobiological profiles to be determined, potential limitations, referrals ...), the worker (background, demographics and psychosocial factors, medical history at work,...), extra-clinical data (organizational profile, demographic profile, psychological profile...) and additional data (diagnostic algorithms, performance algorithms, medical knowledge data bases...) The use of physiological sensors that help us to determine both in the workplace and outside it, the physiological status or health of a worker at a given time at work or outside work (whenever necessary), complements the sensor system module, that is one of the basic elements of the project.

### **5.2. Personal Health Records (PHR)**

300 Risk Management – Current Issues and Challenges

for this to happen, it's necessary to anticipate and predict the occurrence of risk scenarios that can lead to a damaging situation, either by accident or health threat. This need generates a change of paradigm, moving from a reactive system providing management solutions to problems that have already happened and basic preventive measures, to a much more proactive model, where risk management is understood as a mostly preventive tool. To achieve this, it's important to collect, measure and analyze data during a continuous

In this line, future enterprise risk management solution should turn punctual monitoring into a more frequent and personalized vigilance, including individual and collective data. However, collecting information of many people during a long period of time requires collecting a big amount of data. People are not able to process so much information, so intelligent systems for massive data processing are needed. These intelligent systems classify data and generate alarms associated to the worker. Thanks to these alerts and all the other environmental and personal data stored, it is possible to predict health threats. Thus, it

Nowadays, the number of sensors for monitoring personal health data is increasing. In addition, sensors that collect environmental parameters in industrial factories are being introduced more and more. The problem encountered so far, besides a reduced frequency of monitoring, is that these data are usually not thoroughly connected. The information is only collected in order to produce isolated diagnosis or identify single risks, but not common results, and the collected data become less relevant if they are not treated together. The final decision, in a dynamic environment like a factory, could be more precise if results came from a comprehensive study of a diverse set of parameters. However, once the data is collected and stored, a significant effort must be done in processing it in order to identify and highlight those elements that are directly related to present or future risks. This identification becomes more and more difficult as the amount of data analyzed increases and rage of risks augments. Therefore, it seems logical to state that, in parallel to the data collection efforts, new solutions for clustering, prioritizing and filtering information need to be put in place, to generate the most appropriate alarms in the right moment and in the right place. Risks nature can vary from emergency situations to predictive probabilities and Risk Management systems have to be able to discriminate between the two (and the whole range in between) and provide adequate communication of the contextual information so that the reaction to the risk matches the risk characteristics. Finally, once the information has been monitored and classified, the next point is focused on the intervention. With the aim of representing prevention protocols for this intervention, workflows are developed. Given the workers singularity, the adaptation of the prevention protocols is needed for each one of

them. In this way, the elimination of the occupational hazard is much more effective

The Health model established in FASyS, is based on the "Ecological Concept of Disease", in which, the environment (Physical, Social, Economic and Biology, among others) is a set of external conditions and influences affecting the life and development of an organism, human behaviour or society, acting on the balance between the so-called "disease agents"

period of time, in order to evaluate the risks and their evolution.

is possible to act in the most appropriate way for each worker in particular.

Health data has been traditionally produced and owned by the Healthcare Systems and stored in Electronic Healthcare Records (EHR), focusing mainly in describing clinical procedures, tests and values. However, with the purpose of improving the characterization of the person and his environment, EHR data needs to be extended. This new information is stored, together with the EHR information, in other repositories. These repositories are known as PHR (Personal Health Record) [19] and collect data such as habits, preferences, information about the family, work, moods or nutritional profile. These repositories are, in opposition to the EHR, owned by the person, who has the option to share it with whoever he chooses.

The usage of PHRs in occupational health could enable that when an employee goes to work in a company for the first time, the enterprise's health professionals can ask him to share his relevant PHR data, in order to have his personal file more complete and enable a more complete and accurate health risk management. Of course, this situation would require enhanced methods for privacy and data protection, ensuring no unauthorized and adequate usage of the health information is made. In general, current PHRs contain a summarized version of the EHR adapted to the patient's knowledge and needs and, in some cases, home

monitoring data. Future PHRs covering the area of occupational health should be based in the following aspects:

Integrated and Personalised Risk Management in the Sensing Enterprise 303

the high number of rules to be taken into account. In enterprise environments, processes are usually defined as workflows. Workflows [24][12] are formal specifications of processes designed to be automatized. The main advantage of using workflows is that they usually have a graphical interface that makes easier their design and understandability to nonprogramming experts like doctors. In Care Plans environment there are works, available in the literature, which faces this problem using workflows technology [25][26]. The main problem of workflows against other approach like GLIF or traditional techniques is that workflows have less expressivity than them. Nevertheless, there are available workflows approaches in literature [26] that ensures a high expressivity for defining very complex

In addition to graphical design, Workflow has more advantages that can be useful for the design and deployment of care plans. Current workflow systems usually have associated an engine able to automatically execute the processes defined in the graphical way. That means that the formally defined processes can be automatically used for deploying the process by using automatic deploying systems. In addition, Process mining [27] technologies allows the application of pattern recognition technologies to support the iterative design of Care Plans. Furthermore, thanks to the low grammatical complexity of some workflow approaches [27] is possible to apply a great quantity of algorithms and tools for ensuring the completeness, the non- ambiguity and the simulation of processes in order to detect problems in their design before their deployment. In the Figure below is presented a basic specification of a

workflows and even for the design of clinical pathways [27].

Care Plan using a workflow based approach.

**Figure 10.** Example of Workflow Based Clinical Pathway


One of the advantages, for example, would be when a worker goes to work in other factory. If the new factory is enabled, his PHR could be downloaded in the system of the new factory in order to have a more complete file and ensure continuity in the management of the risks for that particular worker. Stored Data can also be extracted for consultations and referrals in case health professionals need to, of course under the corresponding access control that prevents unauthorized sharing of the data. Data can be easily anonymized to be used for statistical and epidemiological studies in order to detect population based health problems.
