Medtronic (Israel)

PurposeThis case study aims to demonstrate the strengths of the Lean Six Sigma (LSS) methodology to improve the acute ischemic stroke (AIS) treatment rates and reduce process lead time at Baruch Padeh Medical Center (BPMC), a rural hospital in the Galilee region of Northern Israel. The LSS project redefined the BPMC stroke care pathway and increased its efficacy.Design/methodology/approachThe LSS methodology was implemented in September 2017 by integrating lean principles and the Six Sigma DMAIC (Define–Measure–Analyze–Improve–Control). Existing procedures, field observation, ad hoc measurement and in-depth interviews were utilized, and the GEMBA method was implemented to identify root cause and improve actions optimizing the stroke pathway.FindingsThe presented case shows the usefulness of the LSS methodology in improving quality performance in a rural hospital. The intervention allowed the BPMC to improve the intravenous tissue plasminogen activator (IV-tPA) administration rate (+15.2%), reducing the process lead time. The lead time of door-to-computer tomography decreased from 52 to 26 min, and the door-to-needle time decreased from 94 to 75 min.Originality/valueThe present case study shows the implementation of the LSS methodology aimed to improve the IV-tPA administration rate and reduce the stroke pathway lead time in a rural hospital. The case demonstrates the potential for the LSS methodology to support the AIS pathway optimization and represents a guide for healthcare organizations located in rural areas.

Continuous electronic monitoring of patient respiratory status frequently includes PetCO2 (end tidal CO2), RR (respiration rate), SpO2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient’s respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO2, RR, PR, and PetCO2 into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts’ scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO2, RR, PR, and PetCO2 readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of −0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3−6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.