Incomplete Data. Each party acknowledges that Data or the Health Data received in response to a Message will not include the Individual’s full and complete medical record or history. Such Data and Health Data will only include that Data and Health Data which is the subject of the Message or the Direct Service secure email and available for exchange through the HIE or sent through the Direct Service in accordance with the Agreement.
Incomplete Data. Each party acknowledges that Data received in response to a Message will not include the Individual’s full and complete medical record or history. Such Data will only include that Data available for exchange through the HIE, External HIE or sent through CRISP Services in accordance with the Agreement.
Incomplete Data. Each party acknowledges that Data received from the HIE in accordance with a Use Case will not include the Individual’s full and complete medical record or history. Such Data will only include that Data which is available for exchange through the HIE, External HIE, or CRISP Services in accordance with the Agreement.
Incomplete Data. During the process of updating job information there may be instances where job information is incomplete. Any relevant additional and/or updated information should be submitted.
Incomplete Data. Each Participant acknowledges that Data received in response to a Message will not include the individual’s full and complete medical record or history. Such Data will only include that Data which is the subject of the Message and available for exchange through the NHIN.
Incomplete Data. During the process of updating job information there may be instances where job information is incomplete. Any relevant additional and/or updated information should be submitted. Market Information. The District will, on a regular basis, review the market information relative to job functions and benchmarks in an effort to maintain updated classifications. Appeal & Reclassification Procedures. The following guidelines apply: Timing. Employees may submit requests for reclassification between March 1st-31st. Written Documentation. The employee will submit the attached Job Classification Review Form. Basis of Request. Terminology and definitions shall be consistent and understood by all parties e.g. essential functions, task/method, requirements, etc. The basis of the request should be guided by factual information on how the job differs from that described. Valid basis for review should be focused on the following: Essential functions performed have changed and are at variance with Job Description. Job requirements have changed and are at variance with Job Description. Standards of performance have changed requiring different skills, knowledge and abilities. Initial decisions were based on inaccurate information. Decision Making Process. The process may vary based on the stated justification for the request, however, assuming that the initial job data is current, the normal review should focus only the following three areas: Appendix 2 (cont.) Classification Appeal Process Guidelines (contd.) Evaluation of published job functions. Analysis should ensure that employee's justification statement of new and/or additional functions is not a semantic restatement of a function, listing of tasks and/or the usual and customary methods of performing the function. Review of the pre-requisite job requirements. Due to organizational changes, new equipment, and/or new technology employees may perceive that the job has changed to an extent justifying re-classification. In today's labor market it is not unusual for 50% of the skills and knowledge required for a job to be out of date within three (3) years. This is a common thread across all industries. New requirements placed on an employee to maintain performance should not alone be the factor guiding re-classification. Measures can be used to determine how changes over time have impacted the skills, knowledge, abilities, responsibilities and working conditions necessary to perform the jobs functions. It is als...
Incomplete Data. The presence and degree of incomplete data is a challenge for traditional data prediction techniques, requiring either exclusion of the entire data record or employ methods to produce a value for the missing datum. Bootstrapping is one such method, (113), although this can gradually introduce errors or bias into the dataset depending on the degree of missing data. ANNs allow incomplete data both during the training phase and also during the testing phase, without the need to create pseudodata, thus maintaining the quality of the initial dataset and allowing flexibility when testing/using the XXX (114).
