Sentence examples for diagnoses service from inspiring English sources

Forty-five (85%) were re-interviewed after 12 months (mean time elapsed = 12.89 months), but of the eight who were not re-interviewed, SRQ data was collected for one and case notes provided details of diagnoses, service use and transitions for all eight.

Information is collected on investigations, diagnoses, services and treatments that result from the referral and any further referrals.

The visit table contains a record per visit (identified uniquely by patient identifier number and visit date), the clinic(s) visited, diagnoses, services provided, plus the amount of money paid.

Service quality measurement plays an important role in assessing service performance, diagnosing service problems, managing service delivery, and determining employee and corporate rewards.

Consequently, they decided to add validation checks for configurations, improve detection, and diagnose service failure.

Previous studies have focused on diagnosing service problems at several levels, including personnel, management, and the supply chain [8, 12].

Working with hybrid technology in the automotive industry requires a strong mechanical aptitude and reasoning skills in order to diagnose, service and do repair work with the more advanced components on electric vehicles.

Post-traumatic stress disorder is the most commonly diagnosed service-related mental disorder among military personnel returning from Iraq and Afghanistan.

Clinical risk prediction models use demographic information, diagnoses, and service use data to stratify a population's risk of having or developing a specified disease, or experiencing an outcome such as emergency admission to hospital [ 1, 2].

Previous work has developed and validated a regression algorithm that uses hospital administrative data to identify Medicaid patients who are at high risk of subsequent re-hospitalization in the following 12 months based on their diagnoses and service utilization history in the prior 3 years [ 12, 13].

In brief, the algorithm can employ hospital or State level administrative data to identify patients who are at high risk of subsequent readmission in the next 12 months based on their ICD-9 diagnoses and service utilization history (e.g., ED visits, inpatient admissions, outpatient clinic visits) over the past four to five years.