An observational study to identify factors associated with readmission and to evaluate the impact of pharmacist validation of discharge prescriptions on readmission rate

To identify demographic and pharmaceutical factors associated with readmission and to determine whether pharmacist validation of discharge prescriptions impacted on readmission rate in a district general hospital. • The average number of items prescribed at discharge and the average age were found to be significantly higher in patients who were readmitted than those who were not, and mandating pharmacist validation of discharge prescriptions was associated with a reduction of around one-fifth in the readmission rate. • The study provides evidence of the patient groups it may be most appropriate for pharmacists to focus on in order to reduce readmissions. Introduction Readmission is a growing problem for the National Health Service. In England the rate has increased by almost one-third over ten years, reaching 11.5% in 2011/12.1 In 2009 the Care Quality Commission reported that 81% of General Practitioners recorded discrepancies in discharge medication information “all” or “most of the time.”2 Whilst pharmacist validation of discharge prescriptions (TTOs) is routine in Calderdale and Huddersfield NHS Foundation Trust, it was previously prompted by the need for supply, and due to the successful implementation of one-stop dispensing theTTOvalidation rate was surprisingly low. The study aimed to identify factors associated with readmission, to quantify the effect of enforcing pharmacist validation of TTOs and to determine whether this impacted on the readmission rate. Methods Retrospective analysis of data from all adults discharged from Calderdale Royal Hospital’s Short Stay Unit between 30th September 2013 and 19th January 2014 (pharmacist validation of TTOs became mandatory during normal working hours from the mid-point). Data collected from TTOs included admission and discharge dates, demographics and pharmaceutical details (e.g. number of items prescribed, number of prescription changes, validation status). The primary outcome measure was 30-day readmission status; readmission interval was the secondary outcome measure. Ethical approval was not required. Results Two hundred eighty-three TTOs were completed during the baseline evaluation: 101 (35.7%) were validated by a pharmacist and 42 (14.8%) resulted in readmission. Two hundred ninety-six TTOs were completed during the intervention evaluation: 223 (75.3%) were validated by a pharmacist and 36 (12.2%) resulted in readmission. The average age of those readmitted (73.2) was seven and a half years older than those not readmitted (65.7) (p < 0.01, 95% CI for the difference 3.20–11.8); patients aged 65 or older were significantly more likely to be readmitted (17.6%, 63/357) than younger patients (6.8%, 15/222) (p < 0.01). The number of prescription changes on the TTO was not found to differ significantly between those who were readmitted and those who were not; however, those readmitted were prescribed an average of two more items at discharge (10.8) than those who were not (8.4) (p < 0.01, 95% CI for the difference 0.989–3.90). The readmission behaviour of patients prescribed seven or less items at discharge (n = 221)was found to differ significantly (p < 0.01) from patients prescribed eight or more (n = 264). Discussion The results indicate where pharmacists may have the most impact on reducing readmissions; specifically patients over 65 years of age and those taking eight or more medicines. Further work is needed to determine whether readmission can be reduced in these groups by application of pharmaceutical interventions and to establish the long term benefits of focusing limited resources. Mandating pharmacist validation ofTTOs in working hours was associated with a substantial increase in proportion validated and a notable reduction in readmission rate. It is acknowledged that the activity of the Trust’s Virtual Ward varied during the study, however there was not a pharmacist on the team at that time; further work will be carried out to determine the influence of this on the results observed

John\u27s Use of the Absolute ΕΓΩ EIMI as a Reflection of the Theology of the Prologue to the Fourth Gospel

The investigation will center on John\u27s intended meaning of έγω είμι as the phrase relates to the unique Prologue which prefaces the Fourth Gospel. It is hoped that, in approaching the Prologue as the thematic key to the Gospel, it will provide guidance in ascertaining the relative purpose of the έγω είμιs as they reflect the purpose of the Gospel as a whole; the Prologue should provide a direction for drawing some conclusions as to John\u27s intent in the use of this phrase

Alien Registration- Upton, John E. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/21666/thumbnail.jp

Alien Registration- Upton, John E. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/21666/thumbnail.jp

A preliminary study identifying prescription factors associated with readmission

The LACE index (1) is used by Calderdale and Huddersfield NHS Foundation Trust to refer patients to a Virtual Ward, providing post discharge support with a view to preventing readmissions. Derivation of the LACE index found the Charlson comorbidity index (CCI) predictive of readmission; conditions in the CCI are likely to be treated with medication

Comparison of modelling techniques for milk-production forecasting

peer-reviewedThe objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions

Daily and seasonal trends of electricity and water use on pasture-based automatic milking dairy farms

peer-reviewedThe objective of this study was to identify the major electricity and water-consuming components of a pasture-based automatic milking (AM) system and to establish the daily and seasonal consumption trends. Electricity and water meters were installed on 7 seasonal calving pasture-based AM farms across Ireland. Electricity-consuming processes and equipment that were metered for consumption included milk cooling components, air compressors, AM unit(s), auxiliary water heaters, water pumps, lights, sockets, automatic manure scrapers, and so on. On-farm direct water-consuming processes and equipment were metered and included AM unit(s), auxiliary water heaters, tubular coolers, wash-down water pumps, livestock drinking water supply, and miscellaneous water taps. Data were collected and analyzed for the 12-mo period of 2015. The average AM farm examined had 114 cows, milking with 1.85 robots, performing a total of 105 milkings/AM unit per day. Total electricity consumption and costs were 62.6 Wh/L of milk produced and 0.91 cents/L, respectively. Milking (vacuum and milk pumping, within-AM unit water heating) had the largest electrical consumption at 33%, followed by air compressing (26%), milk cooling (18%), auxiliary water heating (8%), water pumping (4%), and other electricity-consuming processes (11%). Electricity costs followed a similar trend to that of consumption, with the milking process and water pumping accounting for the highest and lowest cost, respectively. The pattern of daily electricity consumption was similar across the lactation periods, with peak consumption occurring at 0100, 0800, and between 1300 and 1600 h. The trends in seasonal electricity consumption followed the seasonal milk production curve. Total water consumption was 3.7 L of water/L of milk produced. Water consumption associated with the dairy herd at the milking shed represented 42% of total water consumed on the farm. Daily water consumption trends indicated consumption to be lowest in the early morning period (0300–0600 h), followed by spikes in consumption between 1100 and 1400 h. Seasonal water trends followed the seasonal milk production curve, except for the month of May, when water consumption was reduced due to above-average rainfall. This study provides a useful insight into the consumption of electricity and water on a pasture-based AM farms, while also facilitating the development of future strategies and technologies likely to increase the sustainability of AM systems