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Author: admin, 26.01.2014. Category: The Power Of Thinking

Literature and original analysis of healthcare costs have shown that a small proportion of patients consume the majority of healthcare resources.
Literature from different jurisdictions has shown that a relatively small proportion of patients consume the majority of healthcare resources.
Given the impact of these HCUs of healthcare, interventions directed at them may improve overall patient outcomes and quality of life, and reduce healthcare spending.
A proactive approach to addressing the problem of HCUs is to target interventions towards patients who are at risk of becoming HCUs. A number of publications have proposed various methods to predict future HCUs, each advocating a different model, predictor variables and type of data. Building upon previous research, we developed a predictive model to identify patients at risk of becoming high-cost healthcare users in Ontario. The purpose of the model presented in this paper is to predict who will or will not become a high-cost healthcare user in the next year, given various patient-level characteristics in the current year and two previous years. Data from various administrative sources were linked using encrypted health insurance numbers. Information was collected on factors (covariates) that may have an influence on the outcome (becoming a high user). Formal ethics review was not required because de-identified Ministry administrative data were used. Patient characteristics of the model cohort and the validation cohort are presented in Table 1. Odds ratios analysis reveals that age is a strong predictor of becoming a high-cost healthcare user, and there is a clear pattern of substantially increasing risk as age increases. Based on out-of-sample validation, both the ROC curve (Figure 1) and the calibration (goodness-of-fit) curves (Figure 2) show very good out-of-sample model performance. Table 3 also presents sensitivity, specificity and so on for other follow-up cut-off points (such as 1%, 10% and 15%). This is the first attempt in Ontario to develop and validate a tool for predicting patients at risk of becoming high-cost healthcare users. The performance of this model (in terms of sensitivity, for example) in comparison to other published predictive models could be of interest. Please note that these numbers are not precisely comparable because the models predict different outcomes and have different event rates (prevalence); however, the figures provide overall reference points for our predictive model's performance.
Limitations of the current model include a very large number of predictor variables and the heavy data requirements to run the model. In Ontario, there are several challenges to this approach, including concerns over privacy in regard to sharing data with providers.
Actual costs in Ontario are established for each hospital using Ontario Cost Distribution Methodology (OCDM), Ontario Ministry of Health and Long-Term Care.
Diagnosis codes for patients were collected from all care types (all healthcare encounters) for the whole year. Material deprivation primarily portrays variations associated with education, employment and income. Ontario's Health Links initiative aims to facilitate coordination of care at a local level for high-needs patients. The latest statistics from CIPD tell us that employee absence has risen to 6.9 days per employee per year on average in the UK, and only 25% of organisations achieved last year’s absence target.
It would appear that employers now grasp the concept of engagement, but for some reason don’t fully comprehend the intrinsic link that exists between engagement and wellbeing.
It is saddening that in most cases, employee wellbeing only becomes an issue because it affects business. Here’s a one-word glimpse into the reality of the current state of our workforce: Leaveism. Research has found that 76 per cent of employees who have practiced leaveism have done so to avoid being labelled as ‘poor performers’, or because they don’t want to be viewed as being unable to cope with their workload. This ultimately means that a large amount of sickness absence is going under reported, and is distorting both the incidence of sickness in the workplace and worryingly, the ability to fully get to grips with employee wellbeing. You might be asking yourself ‘Why would anyone do that?’…If you’re asking that question, you have probably not worked in an organisation that has a ‘quota of sickness’ which if exceeded supposedly reflects poor performance.
It seems that some companies fail to realise that by creating extra stress around health issues in the workplace, they are only going to create a vicious cycle of illness and absenteeism. As with most issues, one way to address the situation surrounding employee wellbeing is to start with communication.
We would very much appreciate if you could complete our site survey so that we may gain from your experiences and ensure that our future plans and enhancements target your specific needs. A proactive approach is to target interventions towards those patients who are at risk of becoming high-cost users (HCUs). In Ontario, for example, 5% of healthcare users consumed 61% of hospital and home care spending (Rais et al.
This approach is aimed at preventing at-risk patients from becoming HCUs in the first place.
Billings and colleagues (2006) presented a case-finding tool for patients at risk of readmission to hospital and developed an algorithm to identify high-risk patients in the United Kingdom. In Ontario, which has a single-payer government health system (OHIP), all patients have unique health insurance numbers that are recorded in all sectors whenever a patient receives a health service.


Continuous variables for healthcare utilization were categorized based on percentiles (zero category was created first, then median was calculated for the remaining positive values, and two remaining categories were created: less than median, and equal or more than median). Performance of the model was evaluated using C-statistic for predictive ability of the model. Good predictive models should show strong performance in the new (out-of-sample, scored) data, since in-sample performance could be unduly optimistic if the model over-fitted the data (in this case, out-of-sample performance could be very poor). Among the patients that the model predicted to be high-cost healthcare users in 2010, 46.2% were not HCUs in 2009. Similarly, as the material and social deprivation indices increase, the risk of becoming a high-cost user increases. Table 3 presents sensitivity, specificity, positive and negative predictive values, and accuracy for different cut-off points for the validation (out-of-sample) cohort.
Presented results suggest that the performance of the model is very good, and the model has been validated for an out-of-sample validation cohort.
Unfortunately, the authors were not able to identify published studies with reported performance for models predicting high-cost healthcare users.
Models also predicting the top 1% and top 10% HCUs were also explored using reported methodology, and these models showed very strong performance. The Ministry's Health Analytics Branch develops community profiles of populations and high users where the Health Links model is being established. One potential approach is to provide the health card numbers of high-risk patients to primary care providers so that they can implement appropriate prevention strategies, potentially mitigating or avoiding HCU status in the future.
The Ministry's approach with Health Links7 has been to provide aggregate information about HCUs so that providers can identify the patient populations that have historically consumed the most resources. Dummy variables were entered in the model specifying whether a patient has or does not have a disease (ICD-10 or ICD-9 code) in the corresponding ICD-10 chapter (ICD-9 chapters were mapped onto ICD-10 chapters). Social deprivation indicates the state of being separated, divorced or widowed, living alone, or being a member of a single-parent family (Pampalon et al.
One of the goals of the initiative is to provide better care for the 1% to 5% of people who, research has indicated, are high users of healthcare.
Unsurprisingly, the working world is now desperately trying to find out how to address this because of the huge impact that absenteeism has on productivity.
Many organisations are now turning to wellbeing programs in an attempt to combat the ever worsening stress levels, but it has been suggested that for some companies, this is simply a move to tick a box as opposed to a reaction to genuine concern or understanding. Speaking from experience (happily a good few companies ago), taking annual leave rather than sickness leave makes a lot of sense when you are worried about being hauled into an office and grilled about your performance and commitment. While the business impact should of course be taken into account, we need to recognise that this is about a deep-rooted wellbeing problem. This approach requires identifying high-risk patients accurately before substantial avoidable costs have been incurred and health status has deteriorated further. Such an approach requires some mechanism to identify or predict high-risk patients accurately before substantial preventable or avoidable costs have been incurred and health status has deteriorated further (Billings et al.
The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions and clinical condition.
Potential ways to utilize this information in practice and the next steps are also discussed. Patient costs for AIP, ER, DS, Rehab, CCC, MH and HC were derived from actual unit cost1 (actual cost per weighted case) times weighted volume of services (number of weighted cases).
Where applicable, missing values were imputed via the multiple imputation technique (using SAS PROC MI, SAS Institute). Moreover, this is the intended application of predictive models: to apply the model to the new data with unknown outcomes in order to predict them.
If the top 5% patients at risk of becoming HCUs are followed, the achieved sensitivity and specificity is 42.2% and 97%, respectively.
However, the cut-off point for follow-up of patients could be selected at any level (following up 1%, or even 10% of patients with the highest risk of becoming top 5% healthcare users), depending on availability of healthcare funding for follow-up, thus increasing or decreasing the sensitivity, or the number of captured future top 5% high-cost healthcare users.
Special attention could be paid to the factors, listed in the Results section, that are the strongest predictors of becoming HCUs. Nevertheless, we scanned the literature to observe the performance of other predictive models with relevant outcomes that could be an indirect proxy for future high cost, such as hospital admissions in the next year. The final decision as to the most useful cut-off point will depend on the specifics of policy decision-making with regard to practical implementation, and of course on the availability of scarce healthcare dollars for the follow-up. Missing values do not present a significant obstacle, as only three variables have missing values for a very small proportion of patients.
This model could be used to identify patient populations at high risk of becoming high users, so that Health Links could develop interventions that address specific needs of those patient groups. It also aims to reduce costs, particularly expensive hospital visits, based on the assumption that many of these patients' hospital emergency ward visits, admissions and readmissions can be prevented with better coordinated care (Silversides and Laupacis 2013).
The CIPD suggests that the majority of employers are more reactive than proactive in their approach to wellbeing (61 per cent), responding to persistent problems rather than predicting what health and wellbeing factors might impact the workforce in future.
We need to find out what individuals in the workforce really need from their roles and employers in order to stay healthy and motivated, rather than throwing systems at them and asking them why they aren’t happy yet. A study of physician services utilization in British Columbia found that 5% of healthcare users consumed 30% of spending on physician services (Reid et al.
In the United States, Fleishman and Cohen (2010) found that medical condition information improved prediction of high expenditures beyond that obtainable using gender and age.


Data for the analysis were obtained from the Ontario Ministry of Health and Long-Term Care (MOHLTC). Receiver-operating characteristic (ROC) curve and calibration (goodness-of-fit) curves were constructed based on the validation sample.
These values suggest very reasonable predictive power, indicating that the model picks up 42.2% of all high-cost healthcare users and correctly identifies 97% of those who are not high users. Another limitation that we encountered was an inability to access available patient classification systems (such as Adjusted Clinical Groups [ACG] or Diagnostic Cost Groups [DCG]).
An example might be CHF-centred clinics for congestive heart failure patients (Wijeysundera et al. Unfortunately, there is probably no one-size-fits-all resolution at this point, but understanding individual needs and issues is surely a starting point. Ash and colleagues (2001) also found that risk models based on Diagnostic Cost Groups (DCG) were at least as powerful as prior cost for identifying HCUs.
Patient cost for LTC was estimated using average cost per patient per day times patient length of stay. The pattern of the missing data allowed us to assume that the values were missing completely at random (MCAR).
Out-of-sample model performance was evaluated using sensitivity, specificity, positive and negative predictive values, and accuracy on the validation sample (calculated for scenarios if following up on the top 1%, 5%, 10% or 15% of patients with the highest risk of becoming HCUs).
The number of variables removed because of clustering was 28, leaving 69 variables in the final model. There is also a clear pattern of increased risk as the degree of rurality increases (as measured by the Rurality Index of Ontario Score).
Accuracy of 94.2% is also very reasonable (percentage of true positive and true negative out of all patients).
In Manitoba, 5% of prescription drug users accounted for 41% of prescription expenditures (Kozyrskyj et al.
In Ontario, Walraven and colleagues (2010) derived and validated an index to predict early death or unplanned readmission after discharge from hospital. Costs for outpatient oncology, outpatient dialysis and outpatient clinic were not included owing to data quality issues with case mix and cost data in these sectors, and the Ministry's general recommendation not to use these data in funding formulas in Ontario. As a next step, a number of variables were reduced using the variable clustering technique (SAS PROC VARCLUS, SAS Institute).
Cut-off probability levels for different potential follow-up cohorts (if following up 1% of highest risk users, 5%, 10% or 15%) were selected, and the outcome variable was set at 1 if the predicted probability equaled or exceeded that cut-off.
Current and past (1 year ago and 2 years ago) healthcare utilization across different care types are among the strongest predictors of becoming high-cost healthcare users. 2013), which also predicted next-year admissions, achieved 9.2% sensitivity for the top 1% patients follow-up. In order to overcome this obstacle, we used a proxy in our model: all patient ICD-10 and ICD-9 diagnoses were grouped into ICD-10 chapters, with further separation of certain chronic diseases, such as CHF, COPD and diabetes. Another potential approach would require converting this data-intensive model into a simpler one, with fewer variables, and creating a paper- or desktop computer-based tool that can be used by providers themselves in their offices to score patients and identify those at high risk of becoming high users.8 In any scenario, physicians would be informed of at-risk patients to provide timely interventions to mitigate or reduce the number of HCUs, thus improving patient outcomes and saving finite resources.
Users (patients) were sorted in descending order of total expenditures, and the top 5% of users were classified as high-cost healthcare users.
Multiple cut-offs for follow-up are presented (following up 1%, 5%, 10%, 15%) to enable dialogue on the degree of sensitivity that could be achieved if different resources are utilized (assuming that implementation of follow-up interventions requires additional resources). Of particular note are long-stay, long-term care utilization, more than one hospitalization in in-patient mental health, chronic continuing care, acute in-patient care, high number of outpatient dialysis and oncology visits, and high number of services in home care. If the top 5% of patients at risk of becoming HCUs are followed, the sensitivity is 42.2% and specificity is 97%. In Australia, high-cost users (HCUs) accounted for 38% of both in-patient costs and in-patient days (Calver et al. A binary variable was created and added to the data to identify patients as either high users or not.
Alternatives for implementation of the model include collaboration between different levels of healthcare services for personalized healthcare interventions and interventions addressing needs of patient cohorts with high-cost conditions. US data from the Arizona Health Care Cost Containment System showed that 10% of patients accounted for two-thirds of healthcare costs (Moturu et al. It should be noted that our model reports performance in the validation cohort (out-of-sample model performance). Another study in the United States looking at healthcare expenditures from 1928 through 1996 found that the top 5% of HCUs accounted for more than half of health spending in both 1987 and 1996, while the top 10% accounted for about 70% of all healthcare spending (Berk and Monheit 2001).
Table 4 summarizes the information on the performance of the models discussed, based on sensitivity and positive predictive value metrics.
Yet another US study found that 5% of the population accounted for 49% of total healthcare spending (Center for Healthcare Research and Transformation 2010).



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