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Executive dysfunction is common in individuals with substance use disorder (SUD) and presents a barrier to treatment engagement. The study aimed to investigate the effectiveness of cognitive remediation (CR) for improving executive functioning and treatment retention in patients with SUD, using a stepped-wedge cluster randomized controlled trial. The sample included 527 adults enrolled across ten residential SUD treatment providers in NSW, Australia. The intervention consisted of 12 hours of CR delivered over six weeks in a group format. The comparator was treatment-as-usual (TAU). Primary outcomes included self-reported executive functioning and proportion of treatment completed (PoTC), measured as the number of days in treatment divided by the planned treatment duration. Intention-to-treat analysis did not find significant differences for self-reported executive functioning (mean difference = −2.49, 95%CI [−5.07, 0.09], p =.059) or PoTC (adjusted mean ratio = 1.09, 95%CI [0.88, 1.36], p =.442). Due to high dropout from the intention-to-treat sample (56%) a post-hoc analysis was conducted using a per-protocol approach, in which CR was associated with improved self-reported executive functioning (mean difference = −3.33, 95%CI [−6.10, −0.57], p =.019) and improved likelihood of treatment graduation (adjusted odds ratio = 2.43, 95%CI [1.43, 4.11], p <.001). More research is required to develop a CR approach that results in service-wide treatment effectiveness. [ABSTRACT FROM AUTHOR]

Objective: To quantify and examine specialist palliative care (SPC) in-hospital activity and compare it to routinely collected administrative data on palliative care (PC). Methods: All patients discharged from a large acute care tertiary hospital in New South Wales, Australia, between July 1 and December 31, 2017, were identified from the hospital's data warehouse. Administrative data were supplemented with information from the electronic medical record for hospital stays which were assigned the PC additional diagnosis code (Z51.5); had a "palliative care" care type; or included SPC consultation. Results: Of 34 653 hospital stays, 524 were coded as receiving PC—based on care type (43%) and/or diagnosis code Z51.5 (100%). Specialist palliative care provided 1717 consultations over 507 hospital stays. Patients had 2 (median; interquartile range: 1-4) consultations during an average stay of 15.3 days (SD 15.78; median 10); the first occurred 7.0 days (SD 12.13; median 3) after admission. Of patient stays with an SPC consultation, 70% were assigned the PC Z51.5 code; 60% were referred for symptom management; 68% had cancer. One hundred forty-one patients were under a palliative specialist—either from initial hospital admission (49.6%) or later in their stay. Conclusions: Palliative care specialists provide expert input into patient management, benefitting patients and other clinicians. Administrative data inadequately capture their involvement in patient care, especially consultations, and are therefore inappropriate for reporting SPC activity. Exclusion of information related to SPC activity results in an incomplete and distorted representation of PC services and fails to acknowledge the valuable contribution made by SPC. [ABSTRACT FROM AUTHOR]

Objective: Hospital use increases in the last 3 months of life. We aimed to examine its association with where people live and its variation across a large health jurisdiction. Methods: We studied a number of emergency department presentations and days spent in hospital, and in-hospital deaths among decedents who were hospitalized within 30 days of death across 153 areas in New South Wales (NSW), Australia, during 2010-2015. Results: Decedents' demographics and health status were associated with hospital use. Primary care and aged care supply had no or minimal influence, as opposed to the varying effects of areal factors—socioeconomic status, remoteness, and distance to hospital last admitted. Overall, there was an approximate 20% difference in hospital use by decedents across areas. In all, 18% to 57% of areas had hospital use that differed from the average. Discussion: The observed disparity can inform targeted local efforts to strengthen the use of community care services and reduce the burden of end-of-life care on hospitals. [ABSTRACT FROM AUTHOR]

Aims: To describe falls causing injury, types of injuries, and the reporting of these falls. Methods: Administrative and incident reporting datasets between July 2012 and June 2017 were analyzed. Results: Using both datasets, 5,653 falls were identified (3.7 per 1,000 bed-days). Administrative and incident datasets accounted for 40.8% (2,299) and 84.4% (4,752) of falls, respectively. More than half the falls in the administrative data required only observation/examination (1,221 falls; 53.1%, 0.8 per 1,000 bed-days). The remaining 1,078 falls (46.9%; 0.7 per 1,000 bed-days) caused 1,533 injuries (1.0 per 1,000 bed-days). Most injuries happened in patients who were 65 years or older (73.9%), during the first episode of care (79.0%), in the acute care setting (70.4%), or on the same level (60.1%). The most common injuries were to the head (33.3%). Some injuries were serious (169; 11.0%; 0.11 per 1,000 bed-days). Falls resulting in injury in older patients, with a more severe medical condition and at smaller hospitals, were more likely to be recorded in the incident reporting system whereas subsequent falls were less likely to be recorded. Conclusions: Fewer than half of the falls recorded in administrative data resulted in injuries. A tenth of all falls caused serious injuries. [ABSTRACT FROM AUTHOR]

Inter-hospital transfers improve care delivery for which sending and receiving hospitals both accountable for patient outcomes. We aim to measure accuracy in recorded patient transfer information (indication of transfer and hospital identifier) over 2 years across 121 acute hospitals in New South Wales, Australia. Accuracy rate for 127,406 transfer-out separations was 87 per cent, with a low variability across hospitals (10% differences); it was 65 per cent for 151,978 transfer-in admissions with a greater inter-hospital variation (36% differences). Accuracy rate varied by departure and arrival pathways; at receiving hospitals, it was lower for transfer-in admission via emergency department (incidence rate ratio = 0.52, 95% confidence interval: 0.51-0.53) versus direct admission. Transfer-out data were more accurate for transfers to smaller hospitals (incidence rate ratio = 1.06, 95% confidence interval: 1.03-1.08) or re-transfers (incidence rate ratio > 1.08). Incorporation of transfer data from sending and receiving hospitals at patient level in administrative datasets and standardisation of documentation across hospitals would enhance accuracy and support improved attribution of hospital performance measures. [ABSTRACT FROM AUTHOR]