VP132 Cost Effectiveness Of A Predictive Risk Model In Primary Care

2017 ◽  
Vol 33 (S1) ◽  
pp. 209-210
Author(s):  
Helen Snooks ◽  
Alison Porter ◽  
Mark Kingston ◽  
Bridie Evans ◽  
Deborah Burge-Jones ◽  
...  
Keyword(s):  
Primary Care ◽  
Cost Effectiveness ◽  
High Risk ◽  
Health Services ◽  
Risk Stratification ◽  
Financial Burden ◽  
Risk Level ◽  
Emergency Admissions ◽  
Predictive Risk ◽  
Primary Care Practitioners

INTRODUCTION: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.METHODS: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.RESULTS: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.CONCLUSIONS: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.

scholarly journals Implementation of predictive risk stratification to reduce emergency admissions to hospital: experiences of general practitioners and practice managers

2021 ◽  
Author(s):  
Bridie Angela Evans ◽  
Jan Davies ◽  
Jeremy Dale ◽  
Hayley Hutchings ◽  
Mark Kingston ◽  
...  
Keyword(s):  
Primary Care ◽  
Risk Stratification ◽  
Patient Care ◽  
General Practitioners ◽  
Normalisation Process Theory ◽  
Small Scale ◽  
Emergency Admissions ◽  
Number Of Patients ◽  
Short Period ◽  
Predictive Risk

AbstractAimIn a trial evaluating the introduction of a predictive risk stratification model (PRISM) into primary care, we reported statistically significant increases in emergency hospital admissions and use of other NHS services without evidence of benefits to patients or the NHS. The aim of this study was to explore the views and experiences of general practitioners (GPs) and practice managers on incorporating PRISM into routine practice.MethodsWe interviewed 22 GPs and practice managers in 18 participating practices at two timepoints: 3-6 months after PRISM was available in their practice; and at study end, up to 18 months later. We recorded and transcribed interviews and analysed data thematically using Normalisation Process Theory.ResultsRespondents reported that the decision to use PRISM was based mainly on fulfilling reporting requirements for Quality and Outcome Framework (QOF) incentives. Most applied it to a very small number of patients for a short period. Using PRISM entailed technical tasks, information sharing within practice meetings and changes to patient care. These were diverse and generally small scale. Use was inhibited by PRISM not being integrated with practice systems. Respondents’ evaluation of PRISM was mixed: most doubted it had any large scale impact, but many cited examples of impact on individual patient care. They reported increased awareness of patients in high risk groups.ConclusionsQualitative results suggest mixed views of predictive risk stratification in primary care and raised awareness of highest-risk patient groups, potentially affecting unplanned hospital attendance and admissions. To inform future policy, decision-makers need more information about implementation and effects of emergency admissions risk stratification tools in primary and community settings.Trial registrationControlled Clinical Trials no. ISRCTN55538212.

VP172 Clinical Effectiveness Of A Predictive Risk Model In Primary Care

2017 ◽  
Vol 33 (S1) ◽  
pp. 229-229
Author(s):  
Helen Snooks ◽  
Alison Porter ◽  
Mark Kingston ◽  
Alan Watkins ◽  
Hayley Hutchings ◽  
...  
Keyword(s):  
Primary Care ◽  
Service Use ◽  
Risk Model ◽  
Risk Groups ◽  
Risk Level ◽  
Care Practice ◽  
Emergency Admissions ◽  
Intervention Phase ◽  
Predictive Risk ◽  
The Impact

INTRODUCTION: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.METHODS: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.RESULTS: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.CONCLUSIONS: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.

Risk-adapted screening in bladder cancer using the open-access Internet-based questionnaire RiskCheck bladder cancer: First evaluation by the health services research foundation IQUO, Germany.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 263-263
Author(s):  
Gerson Luedecke ◽  
Goetz Geiges ◽  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Open Access ◽  
Health Services Research ◽  
Health Services ◽  
Risk Stratification ◽  
Daily Routine ◽  
Services Research ◽  
Asymptomatic Patients ◽  
Increased Risk

263 Background: Till this day urologists are waiting on symptomatic persons to initiate any diagnostic work-up to identify bladder cancer (BC) patients. In result we diagnose a quarter to a third of our patients as muscle-invasive cancers. The open-access questionnaire RiskCheck bladder cancer (RCBC) was proven in a pilot-study in daily routine from German urologists organized in the health services research foundation IQUO on asymptomatic patients. Methods: The open-access RCBC questionnaire was used in urological offices to check asymptomatic patients for their BC risk exposure (personal, smoking, occupation and medical induced). The tool delivers the classical risk stratification in low- intermediate- and high risk. All people with intermediate and high risk were checked for tumor presence by urine diagnostics and in case of suspect results controlled by cystoscopy. Statistical analysis was made by IBM-SPSS 19 for incidence distribution and correlation between risk stratification and tumor detection was proven by classification tree analysis, significance p < 0.05. Results: Out of 196 checked asymptomatic persons 185 (93.4%) were negative for tumor and 11 had a detectable tumor. In the group of NED 125 (68.1%) persons were classified as low risk, 26 (15.7%) as intermediate and 30 (16.2%) as high risk. Out of the 11 detected tumors 9 were at intermediate or high risk (81.8%). This resulted in an over all detection rate of 5.6% and focused on the risk population of 13.2%. The association of tumor presence and increased risk was significant (p < 0.01). Compared to the western incidence rates this is an increase in effectiveness of 377. Conclusions: Risk-adapted screening in bladder cancer delivers a reasonable approach to diagnose bladder cancer before emerging symptoms. The questionnaire RCBC integrates evidence based bladder cancer inductors, is easy in use and as a open-access tool available in 10 languages via the Internet ( www.riskcheck-bladder-cancer.info) .

Cost-effectiveness of a preventive counseling and support package for postnatal depression

2006 ◽  
Vol 22 (4) ◽  
pp. 443-453 ◽  
Author(s):  
Stavros Petrou ◽  
Peter Cooper ◽  
Lynne Murray ◽  
Leslie L. Davidson
Keyword(s):  
Primary Care ◽  
Cost Effectiveness ◽  
High Risk ◽  
Willingness To Pay ◽  
Postnatal Depression ◽  
Preventive Intervention ◽  
Intervention Group ◽  
Care Group ◽  
Net Benefit ◽  
Health And Social Care

Objectives: This study reports the cost-effectiveness of a preventive intervention, consisting of counseling and specific support for the mother–infant relationship, targeted at women at high risk of developing postnatal depression.Methods: A prospective economic evaluation was conducted alongside a pragmatic randomized controlled trial in which women considered at high risk of developing postnatal depression were allocated randomly to the preventive intervention (n = 74) or to routine primary care (n = 77). The primary outcome measure was the duration of postnatal depression experienced during the first 18 months postpartum. Data on health and social care use by women and their infants up to 18 months postpartum were collected, using a combination of prospective diaries and face-to-face interviews, and then were combined with unit costs (£, year 2000 prices) to obtain a net cost per mother–infant dyad. The nonparametric bootstrap method was used to present cost-effectiveness acceptability curves and net benefit statistics at alternative willingness to pay thresholds held by decision makers for preventing 1 month of postnatal depression.Results: Women in the preventive intervention group were depressed for an average of 2.21 months (9.57 weeks) during the study period, whereas women in the routine primary care group were depressed for an average of 2.70 months (11.71 weeks). The mean health and social care costs were estimated at £2,396.9 per mother–infant dyad in the preventive intervention group and £2,277.5 per mother–infant dyad in the routine primary care group, providing a mean cost difference of £119.5 (bootstrap 95 percent confidence interval [CI], −535.4, 784.9). At a willingness to pay threshold of £1,000 per month of postnatal depression avoided, the probability that the preventive intervention is cost-effective is .71 and the mean net benefit is £383.4 (bootstrap 95 percent CI, −£863.3–£1,581.5).Conclusions: The preventive intervention is likely to be cost-effective even at relatively low willingness to pay thresholds for preventing 1 month of postnatal depression during the first 18 months postpartum. Given the negative impact of postnatal depression on later child development, further research is required that investigates the longer-term cost-effectiveness of the preventive intervention in high risk women.

Risk stratification in breast cancer screening: Cost‐effectiveness and harm‐benefit ratios for low‐risk and high‐risk women

2020 ◽  
Vol 147 (11) ◽  
pp. 3059-3067
Author(s):  
Valérie D. V. Sankatsing ◽  
Nicolien T. Ravesteyn ◽  
Eveline A. M. Heijnsdijk ◽  
Mireille J. M. Broeders ◽  
Harry J. Koning
Keyword(s):  
Breast Cancer ◽  
Cost Effectiveness ◽  
Cancer Screening ◽  
High Risk ◽  
Risk Stratification ◽  
Breast Cancer Screening ◽  
Low Risk ◽  
High Risk Women ◽  
Screening Cost

scholarly journals Primary care practices’ ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
David A. Dorr ◽  
Rachel L. Ross ◽  
Deborah Cohen ◽  
Devan Kansagara ◽  
Katrina Ramsey ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Adverse Outcomes ◽  
Risk Scores ◽  
Ed Visits ◽  
Care Practices ◽  
Primary Care Practices

Abstract Background Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores. Methods Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score. Results In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71–0.88) but sensitivity and PPV were low (0.16–0.40). Practice-created scores had 0.02–0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity. Conclusions Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

scholarly journals Risk stratification in GIST: shape quantification with CT is a predictive factor

2020 ◽  
Vol 30 (4) ◽  
pp. 1856-1865
Author(s):  
Sheng-cai Wei ◽  
Liang Xu ◽  
Wan-hu Li ◽  
Yun Li ◽  
Shou-fang Guo ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Independent Predictor ◽  
Binary Logistic Regression ◽  
Risk Level ◽  
Binary Logistic Regression Analysis ◽  
Intermediate Risk ◽  
Chi Square ◽  
Ki 67 ◽  
Tumor Shape

Abstract Background Tumor shape is strongly associated with some tumor’s genomic subtypes and patient outcomes. Our purpose is to find the relationship between risk stratification and the shape of GISTs. Methods A total of 101 patients with primary GISTs were confirmed by pathology and immunohistochemistry and underwent enhanced CT examination. All lesions’ pathologic sizes were 1 to 10 cm. Points A and B were the extremities of the longest diameter (LD) of the tumor and points C and D the extremities of the small axis, which was the longest diameter perpendicular to AB. The four angles of the quadrangle ABCD were measured and each angle named by its summit (A, B, C, D). For regular lesions, we took angles A and B as big angle (BiA) and small angle (SmA). For irregular lesions, we compared A/B ratio and D/C ratio and selected the larger ratio for analysis. The chi-square test, t test, ROC analysis, and hierarchical or binary logistic regression analysis were used to analyze the data. Results The BiA/SmA ratio was an independent predictor for risk level of GISTs (p = 0.019). With threshold of BiA at 90.5°, BiA/SmA ratio at 1.35 and LD at 6.15 cm, the sensitivities for high-risk GISTs were 82.4%, 85.3%, and 83.8%, respectively; the specificities were 87.1%, 71%, and 77.4%, respectively; and the AUCs were 0.852, 0.818, and 0.844, respectively. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could (p < 0.05). Shape and Ki-67 were independent predictors of the mitotic value (p = 0.036 and p < 0.001, respectively), and the accuracy was 87.8%. Conclusions Quantifying tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs, especially for high-risk grading and mitotic value > 5/50HPF. Key Points • The BiA/SmA ratio was an independent predictor affecting the risk level of GISTs. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could. • Shape and Ki-67 were independent predictors of the mitotic value. • The method for quantifying the tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs.

scholarly journals Predictive risk modelling under different data access scenarios: who is identified as high risk and for how long?

BMJ Open ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. e018909 ◽  
Author(s):  
Tracy L Johnson ◽  
Jill Kaldor ◽  
Michael O Falster ◽  
Kim Sutherland ◽  
Jacob Humphries ◽  
...  
Keyword(s):  
Primary Care ◽  
High Risk ◽  
Data Access ◽  
Data Availability ◽  
Care Practice ◽  
Full Model ◽  
Risk Models ◽  
Risk Status ◽  
Risk Modelling ◽  
Predictive Risk

ObjectiveThis observational study critically explored the performance of different predictive risk models simulating three data access scenarios, comparing: (1) sociodemographic and clinical profiles; (2) consistency in high-risk designation across models; and (3) persistence of high-risk status over time.MethodsCross-sectional health survey data (2006–2009) for more than 260 000 Australian adults 45+ years were linked to longitudinal individual hospital, primary care, pharmacy and mortality data. Three risk models predicting acute emergency hospitalisations were explored, simulating conditions where data are accessed through primary care practice management systems, or through hospital-based electronic records, or through a hypothetical ‘full’ model using a wider array of linked data. High-risk patients were identified using different risk score thresholds. Models were reapplied monthly for 24 months to assess persistence in high-risk categorisation.ResultsThe three models displayed similar statistical performance. Three-quarters of patients in the high-risk quintile from the ‘full’ model were also identified using the primary care or hospital-based models, with the remaining patients differing according to age, frailty, multimorbidity, self-rated health, polypharmacy, prior hospitalisations and imminent mortality. The use of higher risk prediction thresholds resulted in lower levels of agreement in high-risk designation across models and greater morbidity and mortality in identified patient populations. Persistence of high-risk status varied across approaches according to updated information on utilisation history, with up to 25% of patients reassessed as lower risk within 1 year.Conclusion/implicationsSmall differences in risk predictors or risk thresholds resulted in comparatively large differences in who was classified as high risk and for how long. Pragmatic predictive risk modelling design decisions based on data availability or projected high-risk patient numbers may therefore influence individuals identified as high-risk, overall case mix and risk persistence. Routine data linkage would enable greater flexibility in developing and optimising predictive risk models appropriate to both case-finding and performance measurement applications.

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