The Finnish National Asthma Program 1994-2004 markedly improved asthma care in the 1990s. We evaluated the changes in costs during 26 years from 1987 to 2013. Direct and indirect costs were calculated by using data from national registries. Costs from both the societal and patient perspectives were included. The costs were based on patients with persistent, physician-diagnosed asthma verified by lung function measurements. We constructed minimum and maximum scenarios to assess the effect of improved asthma care on total costs. The number of patients with persistent asthma in the national drug reimbursement register increased from 83,000 to 247,583. Improved asthma control reduced health care use and disability, resulting in major cost savings. Despite a 3-fold increase in patients, the total costs decreased by 14%, from €222 million to €191 million. Costs for medication and primary care visits increased, but overall annual costs per patient decreased by 72%, from €2656 to €749. The theoretical total cost savings for 2013, comparing actual with predicted costs, were between €120 and €475 million, depending on the scenario used. The Finnish Asthma Program resulted in significant cost savings at both the societal and patient levels during a 26-year period.

Purpose This study aims to report the design and testing of a maturity model for information and knowledge management in the public sector, intended for use in frequent monitoring, trend analysis and in-depth analysis of the contemporary information and knowledge management practices of an organization. Design/methodology/approach A design science approach was used to develop the proposed model. Creation of the model was based on an extensive literature review. Testing of the model was implemented as a survey receiving 37 responses from nine organizations organizing and purchasing public services. Findings The study presents four alternative profiles for an organization’s status, novice, experimenter, facilitator and advanced exploiter, and investigates the differences between these profiles on the basis of the empirical data gathered. The model was found to be both a valid and practical way to determine the state of an organization’s information and knowledge management and identify development needs. Research limitations/implications Testing was conducted in the Finnish public sector and further studies applying the model could be implemented in other countries. The model presented was designed specifically for the public sector and more research is needed to test its applicability in the private sector. Originality/value Maturity models are useful when evaluating information and knowledge management status in an organization, and beneficial for improving organizational performance. The proposed maturity model combines the fields of knowledge management and information management and contributes to the literature with an overarching maturity model that includes a dimension of satisfaction with the organizational maturity level. While many earlier models originate from the consultancy business, the model presented here was also designed for research purposes and tested in practice.

Background: In order to avoid unnecessary use of hospital services at the end-of-life, palliative care should be initiated early enough in order to have sufficient time to initiate and carry out good quality advance care planning (ACP). This single center study assesses the impact of the PC decision and its timing on the use of hospital services at EOL and the place of death. Methods: A randomly chosen cohort of 992 cancer patients treated in a tertiary hospital between Jan 2013-Dec 2014, who were deceased by the end of 2014, were selected from the total number of 2737 identified from the hospital database. The PC decision (the decision to terminate life-prolonging anticancer treatments and focus on symptom centered palliative care) and use of PC unit services were studied in relation to emergency department (ED) visits, hospital inpatient days and place of death. Results: A PC decision was defined for 82% of the patients and 37% visited a PC unit. The earlier the PC decision was made, the more often patients had an appointment at the PC unit (> 180 days prior to death 72% and < 14 days 10%). The number of ED visits and inpatient days were highest for patients with no PC decision and lowest for patients with both a PC decision and an PC unit appointment (60 days before death ED visits 1.3 vs 0.8 and inpatient days 9.9 vs 2.9 respectively, p < 0.01). Patients with no PC decision died more often in secondary/tertiary hospitals (28% vs. 19% with a PC decision, and 6% with a decision and an appointment to a PC unit). Conclusions: The PC decision to initiate a palliative goal for the treatment had a distinct impact on the use of hospital services at the EOL. Contact with a PC unit further increased the likelihood of EOL care at primary care.

A nationwide, long-term and systematic lung health policy has been implemented in Finland. The real-world data indicate that the burden of respiratory diseases has reduced both for patients and, overall, for society. https://bit.ly/3LvPjjv

Background:In previous years a substantial number of studies have identified statistically important predictors of nursing home admission (NHA). However, as far as we know, the analyses have been done at the population-level. No prior research has analysed the prediction accuracy of a NHA model for individuals. Methods: This study is an analysis of 3056 longer-term home care customers in the city of Tampere, Finland. Data were collected from the records of social and health service usage and RAI-HC (Resident Assessment InstrumentHome Care) assessment system during January 2011 and September 2015. The aim was to find out the most efficient variable subsets to predict NHA for individuals and validate the accuracy. The variable subsets of predicting NHA were searched by sequential forward selection (SFS) method, a variable ranking metric and the classifiers of logistic regression (LR), support vector machine (SVM) and Gaussian naive Bayes (GNB). The validation of the results was guaranteed using randomly balanced data sets and cross-validation. The primary performance metrics for the classifiers were the prediction accuracy and AUC (average area under the curve). Results: The LR and GNB classifiers achieved 78% accuracy for predicting NHA. The most important variables were RAI MAPLE (Method for Assigning Priority Levels), functional impairment (RAI IADL, Activities of Daily Living), cognitive impairment (RAI CPS, Cognitive Performance Scale), memory disorders (diagnoses G30-G32 and F00-F03) and the use of community-based health-service and prior hospital use (emergency visits and periods of care). Conclusion:The accuracy of the classifier for individuals was high enough to convince the officials of the city of Tampere to integrate the predictive model based on the findings of this study as a part of home care information system. Further work need to be done to evaluate variables that are modifiable and responsive to interventions.