A Quality Improvement project was conducted in Ty Llidiard, a 15-bed adolescent unit in South Wales, over a three-month period to introduce and embed outcome measurement from the perspective of young people (YP) admitted to the unit. This was done to meet two standards set by Quality Network for Inpatient CAMHS: 1. “Outcome measurement tools are completed from the perspective of young people”, 2. “The ward team use quality improvement methods to work on service improvements.” Through use of these measures we hoped to give the YP a stronger voice in the multidisciplinary ward round (MDT-WR) and to improve their experience of attending it.

A validated patient-rated outcome measure (PROM) and experience measure (PREM) were chosen and adapted in co-production with the YP on the ward. The measures were selected on the basis of being quickly and easily understood and completed. The PROM provided useful ratings in key areas (“school”, “home”, “family”, “me”) and the PREM measured how patients experienced attending the MDT-WR. Outcomes and completion rate of these measures were audited weekly for three months, and several Plan-Do-Study-Act (PDSA) cycles were completed to increase their usage and to improve the YP's experience of attending their MDT-WR.

From an initial baseline of 0% we achieved a maximum of 50% of YP on the ward completing a PROM each week, and a maximum of 100% of YP who attended MDT-WR completing a PREM. Three PDSA cycles improved our completion rate. PROM ratings were used as part of the clinical discussion in MDT-WR each week. Not all YP were well enough to complete it, but for those who were its clinical usefulness (and especially comparing scores in consecutive weeks) was reported in clinician feedback. PREM scores were presented and discussed a number of times in ward improvement meetings and management meetings and four PDSA cycles completed which incrementally increased the PREM average weekly scores.

We were successful in introducing and then increasing the completion rate of patient rated measures. Through use of the PREM we were able to make changes to the ward round and demonstrate the subsequent improvements to the experience of the patients attending. We have demonstrated that outcome measurement from the perspective of YP can contribute usefully to the MDT-WR and can improve YP's experience of the service. Use of these measures will continue as standard practice on our unit and could contribute usefully in future service evaluation projects.

Lithium is viewed as the first-line long-term treatment for prevention of relapse in people with bipolar disorder.

This study examined factors associated with the likelihood of maintaining serum lithium levels within the recommended range and explored whether the monitoring interval could be extended in some cases.

We included 46 555 lithium rest requests in 3371 individuals over 7 years from three UK centres. Using lithium results in four categories (<0.4 mmol/L; 0.40–0.79 mmol/L; 0.80–0.99 mmol/L; ≥1.0 mmol/L), we determined the proportion of instances where lithium results remained stable or switched category on subsequent testing, considering the effects of age, duration of lithium therapy and testing history.

For tests within the recommended range (0.40–0.99 mmol/L categories), 84.5% of subsequent tests remained within this range. Overall, 3 monthly testing was associated with 90% of lithium results remaining within range, compared with 85% at 6 monthly intervals. In cases where the lithium level in the previous 12 months was on target (0.40–0.79 mmol/L; British National Formulary/National Institute for Health and Care Excellence criteria), 90% remained within the target range at 6 months. Neither age nor duration of lithium therapy had any significant effect on lithium level stability. Levels within the 0.80–0.99 mmol/L category were linked to a higher probability of moving to the ≥1.0 mmol/L category (10%) compared with those in the 0.4–0.79 mmol/L group (2%), irrespective of testing frequency.

We propose that for those who achieve 12 months of lithium tests within the 0.40–0.79 mmol/L range, the interval between tests could increase to 6 months, irrespective of age. Where lithium levels are 0.80–0.99 mmol/L, the test interval should remain at 3 months. This could reduce lithium test numbers by 15% and costs by ~$0.4 m p.a.

Lithium was first found to have an acute antimanic effect in 1948 with further corroboration in the early 1950s. It took some time for lithium to become the standard treatment for relapse prevention in bipolar affective disorder. In this study, our aims were to examine the factors associated wtih the likelihood of maintaining lithium levels within the recommended therapeutic range and to look at the stability of lithium levels between blood tests. We examined this relation using clinical laboratory serum lithium test requesting data collected from three large UK centres, where the approach to managing patients with bipolar disorder and ordering lithium testing varied.

46,555 lithium rest requests in 3,371 individuals over 7 years were included from three UK centres. Using lithium results in four categories (<0.4 mmol/L; 0.40–0.79 mmol/L; 0.80–0.99 mmol/L; ≥1.0 mmol/L), we determined the proportion of instances where, on subsequent testing, lithium results remained in the same category or switched category. We then examined the association between testing interval and proportion remaining within target, and the effect of age, duration of lithium therapy and testing history.

For tests within the recommended range (0.40–0.99 mmol/L categories), 84.5% of subsequent tests remained within this range. Overall 3-monthly testing was associated with 90% of lithium results remaining within range compared with 85% at 6-monthly intervals. At all test intervals, lithium test result history in the previous 12-months was associated with the proportion of next test results on target (BNF/NICE criteria), with 90% remaining within range target after 6-months if all tests in the previous 12-months were on target. Age/duration of lithium therapy had no significant effect on lithium level stability. Levels within the 0.80–0.99 mmol/L category were linked to a higher probability of moving to the ≥1.0 mmol/L category (10%) than those in the 0.40–0.79 mmolL group (2%), irrespective of testing frequency. Thus prior history in relation to stability of lithium level in the previous 12 months is a predictor of future stability of lithium level.

We propose that, for those who achieve 12-months of lithium tests within the 0.40–0.79mmol/L range, it would be reasonable to increase the interval between tests to 6 months, irrespective of age, freeing up resource to focus on those less concordant with their lithium monitoring. Where lithium level is 0.80–0.99mmol/L test interval should remain at 3 months. This could reduce lithium test numbers by 15% and costs by ~$0.4 m p.a.

New approaches are needed to safely reduce emergency admissions to hospital by targeting interventions effectively in primary care. A predictive risk stratification tool (PRISM) identifies each registered patient's risk of an emergency admission in the following year, allowing practitioners to identify and manage those at higher risk. We evaluated the introduction of PRISM in primary care in one area of the United Kingdom, assessing its impact on emergency admissions and other service use.

We conducted a randomized stepped wedge trial with cluster-defined control and intervention phases, and participant-level anonymized linked outcomes. PRISM was implemented in eleven primary care practice clusters (total thirty-two practices) over a year from March 2013. We analyzed routine linked data outcomes for 18 months.

We included outcomes for 230,099 registered patients, assigned to ranked risk groups.

Overall, the rate of emergency admissions was higher in the intervention phase than in the control phase: adjusted difference in number of emergency admissions per participant per year at risk, delta = .011 (95 percent Confidence Interval, CI .010, .013). Patients in the intervention phase spent more days in hospital per year: adjusted delta = .029 (95 percent CI .026, .031). Both effects were consistent across risk groups.

Primary care activity increased in the intervention phase overall delta = .011 (95 percent CI .007, .014), except for the two highest risk groups which showed a decrease in the number of days with recorded activity.

Introduction of a predictive risk model in primary care was associated with increased emergency episodes across the general practice population and at each risk level, in contrast to the intended purpose of the model. Future evaluation work could assess the impact of targeting of different services to patients across different levels of risk, rather than the current policy focus on those at highest risk.

Emergency admissions to hospital are a major financial burden on health services. In one area of the United Kingdom (UK), we evaluated a predictive risk stratification tool (PRISM) designed to support primary care practitioners to identify and manage patients at high risk of admission. We assessed the costs of implementing PRISM and its impact on health services costs. At the same time as the study, but independent of it, an incentive payment (‘QOF’) was introduced to encourage primary care practitioners to identify high risk patients and manage their care.

We conducted a randomized stepped wedge trial in thirty-two practices, with cluster-defined control and intervention phases, and participant-level anonymized linked outcomes. We analysed routine linked data on patient outcomes for 18 months (February 2013 – September 2014). We assigned standard unit costs in pound sterling to the resources utilized by each patient. Cost differences between the two study phases were used in conjunction with differences in the primary outcome (emergency admissions) to undertake a cost-effectiveness analysis.

We included outcomes for 230,099 registered patients. We estimated a PRISM implementation cost of GBP0.12 per patient per year.

Costs of emergency department attendances, outpatient visits, emergency and elective admissions to hospital, and general practice activity were higher per patient per year in the intervention phase than control phase (adjusted δ = GBP76, 95 percent Confidence Interval, CI GBP46, GBP106), an effect that was consistent and generally increased with risk level.

Despite low reported use of PRISM, it was associated with increased healthcare expenditure. This effect was unexpected and in the opposite direction to that intended. We cannot disentangle the effects of introducing the PRISM tool from those of imposing the QOF targets; however, since across the UK predictive risk stratification tools for emergency admissions have been introduced alongside incentives to focus on patients at risk, we believe that our findings are generalizable.

A predictive risk stratification tool (PRISM) to estimate a patient's risk of an emergency hospital admission in the following year was trialled in general practice in an area of the United Kingdom. PRISM's introduction coincided with a new incentive payment (‘QOF’) in the regional contract for family doctors to identify and manage the care of people at high risk of emergency hospital admission.

Alongside the trial, we carried out a complementary qualitative study of processes of change associated with PRISM's implementation. We aimed to describe how PRISM was understood, communicated, adopted, and used by practitioners, managers, local commissioners and policy makers. We gathered data through focus groups, interviews and questionnaires at three time points (baseline, mid-trial and end-trial). We analyzed data thematically, informed by Normalisation Process Theory (1).

All groups showed high awareness of PRISM, but raised concerns about whether it could identify patients not yet known, and about whether there were sufficient community-based services to respond to care needs identified. All practices reported using PRISM to fulfil their QOF targets, but after the QOF reporting period ended, only two practices continued to use it. Family doctors said PRISM changed their awareness of patients and focused them on targeting the highest-risk patients, though they were uncertain about the potential for positive impact on this group.

Though external factors supported its uptake in the short term, with a focus on the highest risk patients, PRISM did not become a sustained part of normal practice for primary care practitioners.