Defining business model key performance indicators using intentional linguistic summaries.

To sustain competitiveness in contemporary, fast-paced markets, organizations increasingly focus on innovating their business models to enhance current value propositions or to explore novel sources of value creation. However, business model innovation is a complex task, characterized by shifting characteristics in terms of uncertainty, data availability and its impact on decision making. To cope with such challenges, business model evaluation is advocated to make sense of novel business models and to support decision making. Key performance indicators (KPIs) are frequently used in business model evaluation to structure the performance assessment of these models and to evaluate their strategic implications, in turn aiding business model decision making. However, given the shifting characteristics of the innovation process, the application and effectiveness of KPIs depend significantly on how such KPIs are defined. The techniques proposed in the existing literature typically generate or use quantitatively oriented KPIs, which are not well-suited for the early phases of the business model innovation process. Therefore, following a design science research methodology, we have developed a novel method for defining business model KPIs, taking into account the characteristics of the innovation process, offering holistic support toward decision making. Building on theory on linguistic summarization, we use a set of structured templates to define qualitative KPIs that are suitable to support early-phase decision making. In addition, we show how these KPIs can be gradually quantified to support later phases of the innovation process. We have evaluated our method by applying it in two real-life business cases, interviewing 13 industry experts to assess its utility.

Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers.

PURPOSE: The focus of bariatric surgery is reduction of weight, reflected in body mass index (BMI). However, the resolution of comorbidity is a second important outcome indicator. The degree of comorbidity is hard to quantify objectively as comorbidities develop gradually and are interdependent. Multiple scoring systems quantifying comorbidity exist but they lack continuity and objectivity. In analogy with BMI as index for weight, the Metabolic Health Index (MHI) is developed as objective quantification of metabolic health status. Laboratory data were used as comorbidities affect biomarkers. Conversely, laboratory data can be used as objectively obtained variables to describe comorbidity. METHODS: Laboratory data were collected and crosschecked by national quality registry entries. Machine learning was applied to develop an ordinal logistic regression model, using 4 clinical and 32 laboratory input variables. The output was mathematically transformed into a continuous score for intuitive interpretation, ranging from 1 to 6 (MHI). RESULTS: In total, 4778 data records of 1595 patients were used. The degree of comorbidity is best described by age at phlebotomy, estimated Glomerular Filtration Rate (eGFR), and concentrations of glycated hemoglobin (HbA1c), triglycerides, and potassium. The model is independent of day of sampling and type of surgery. Mean MHI was significantly different between patient subgroups with increasing number of comorbidities. CONCLUSION: The MHI reflects severity of comorbidity, enabling objective assessment of a bariatric patient's metabolic health state, regardless day of sampling and surgery type. Next to weight-focused outcome measures like %TWL, the MHI can serve as outcome measure for metabolic health.

Improved testing for vitamin B12 deficiency: correcting MMA for eGFR reduces the number of patients classified as vitamin B12 deficient.

Background Methylmalonic acid (MMA) can detect functional vitamin B12 deficiencies as it accumulates early when intracellular deficits arise. However, impaired clearance of MMA from blood due to decreased glomerular filtration rate (eGFR) also results in elevated plasma MMA concentrations. Alternative to clinical trials, a data mining approach was chosen to quantify and compensate for the effect of decreased eGFR on MMA concentration. Methods Comprehensive data on patient's vitamin B12, eGFR and MMA concentrations were collected ( n = 2906). The relationship between vitamin B12, renal function (eGFR) and MMA was modelled using weighted multiple linear regression. The obtained model was used to estimate the influence of decreased eGFR on MMA. Clinical impact was examined by comparing the number of patients labelled vitamin B12 deficient with and without adjustment in MMA. Results Adjusting measured MMA concentrations for eGFR in the group of patients with low-normal vitamin B12 concentrations (90-300 pmol/L) showed that the use of unadjusted MMA concentrations overestimates vitamin B12 deficiency by 40%. Conclusions Through a data mining approach, the influence of eGFR on the relation between MMA and vitamin B12 can be quantified and used to correct the measured MMA concentration for decreased eGFR. Especially in the elderly, eGFR-based correction of MMA may prevent over-diagnosis of vitamin B12 deficiency and corresponding treatment.