Showing posts with label hospital information system. Show all posts
Showing posts with label hospital information system. Show all posts

Increasing Time Spent With Patients And Improving Relationships : A Better Bedside Manner With iPad

Bedside care is a vital part of health care professionals' relationships with their patients at the hospital. But the staff at hospital found that modern technology sometimes made those interactions more difficult. When Apple introduced iPad, physicians at the hospital knew they'd found a solution.
A custom iPad app enables physicians and nurses to perform important tasks from the bedside, increasing time spent with patients and improving relationships.
The inability to bring information to the bedside meant physicians had to constantly shuttle between patients and tethered PCs to get status updates, schedule surgeries, prescribe drugs, view X-rays, and perform other important tasks. Since the introduction of technology in this industry physicians have been tethered to devices like PCs and forced to go seek information. Even a laptop wasn't truly mobile.
With iPad, the hospital's doctors and nurses have bedside access to everything they need, and can remain in contact with patients and their families while viewing information that is critical to their care. They can answer patients' questions immediately and make decisions about what's going to be done, with the most current information available. Nothing beats being able to use an app to pull up an X-ray on the device.
Not only has iPad increased efficiency from a provider perspective—it's increased engagement between the provider and patient.

Implementation of Secure Database On a Hospital

A hospital, with health care information systems is one case of a security critical environment. It is one of the few environments, in which a confidentiality breach, wrong information or even a relatively minor loss of access to information may be life-threatening. Security is therefore an important issue which encompasses all aspects of the organization, from patient and staff safety to deeply personal information about staff and patients that is distributed throughout the organization. Due to the widespread use of the database technology, database security plays today a significant role in the overall security of health care information systems.
The development of a secure database for health care information system requires an appropriate multiphase design methodology which will guide the steps of the development and will provide tools supporting the automatic execution of some steps. The proposed methodology and security policy helps ensure all three aspects of security (secrecy, confidentiality and availability), without introducing significant overheads. It is based on the integration of mandatory and discretionary security policies and takes security into consideration from the very first steps of the design.
The choice of an appropriate security policy and a suitable secure database design methodology is crucial in each of health care environments. The two most well known proposals for the database security policies are the mandatory and the discretionary ones. Discretionary security policies govern the access of users to the information on the basis of the user’s identity and the rules specifying, for each user and each object in the system, the types of access the user is allowed on the object. Discretionary security policies are flexible and suitable for a variety of implementations. However, they provide insufficient control of the information flow (e.g. they are vulnerable to malicious attacks such as the Trojan Horses). On the other hand, mandatory security policies provide a high level of certification for security, based on the use of unforgeable security labels, which are assigned both to users and data. Thus, they allow one to track the flow of information. They are however mainly suitable to certain kinds of environments where the users and the objects can be easily classified (i.e. the military one).
None of these two major policies are sufficient by itself to cover the security needs of the health care environments. Hence, it has been necessary to propose a new security policy, which has been based on the integration of mandatory and discretionary control policies. In order to maximize the effectiveness and decrease the complexity of implementing this policy, a step by step design methodology with integrated security has been proposed. In particular, the responsibility of the role in the application determines the security label (clearance) of the user role. The security label (classification) of the data represents its level of sensitivity. The user roles are assigned a node at the user role hierarchy (URH). Then, beginning from the lower level of the hierarchy, the data items are assigned a security label equal to that of the users that must be cleared to access it. In the end, the security requirements are examined. This may lead to fragmentation of some relations and/or upgrading (since we support tuple level granularity). It must be noted that polyinstantiation - which is a characteristic of the multilevel security policy - is supported only in the form of cover stories. This is possible, due to the support of the write down mechanism (with no fear of inference), which is essential for the hospital environment.

Data Mining Process in Hospital Information System

Data mining aims at discovering novel, interesting and useful knowledge from databases. Conventionally, the data is analyzed manually. Many hidden and potentially useful relationships may not be recognized by the analyst. Many organizations including modern hospitals are capable of generating and collecting a huge amount of data.
Data stored in medical databases are growing in an increasingly rapid way. Analyzing that data is crucial for medical decision making and management. It has been widely recognized that medical data analysis can lead to an enhancement of health care by improving the performance of patient management tasks. There are two main aspects that define the need for medical data analysis.
  1. Support of specific knowledge-based problem solving activities through the analysis of patients’ raw data collected in monitoring.
  2. Discovery of new knowledge that can be extracted through the analysis ofrepresentative collections of example cases, described by symbolic or numericdescriptors.
For these purposes, the increase in database size makes traditional manual data analysis to be insufficient. New research fields such as knowledge discovery in databases (KDD) have rapidly grown in recent years. The main step in the knowledge discovery process, called data mining, deals with the problem of finding interesting regularities and patterns in data.
A simple data mining process model mainly includes 6 steps:
  1. Assembling the data.
  2. The data warehouse.
  3. Relational database and flat files.
  4. Mining the data.
  5. Interpreting the results.
  6. Result application.
We will study each step in later articles.