Disruption vs. evolution in laboratory medicine. Current challenges and possible strategies, making laboratories and the laboratory specialist profession fit for the future

Since beginning of medical diagnostics, laboratory specialists have done an amazing job, continuously improving quality, spectrum and speed of laboratory tests, currently contributing to the majority of medical decision making. These improvements are mostly of an incremental evolutionary fashion, meaning improvements of current processes. Sometimes these evolutionary innovations are of a radical fashion, such as the invention of automated analyzers replacing manual testing or the implementation of mass spectrometry, leading to one big performance leap instead of several small ones. In few cases innovations may be of disruptive nature. In laboratory medicine this would be applicable to digitalization of medicine or the decoding of the human genetic material. Currently, laboratory medicine is again facing disruptive innovations or technologies, which need to be adapted to as soon as possible. One of the major disruptive technologies is the increasing availability and medical use of artificial intelligence. It is necessary to rethink the position of the laboratory specialist within healthcare settings and the added value he or she can provide to patient care. The future of the laboratory specialist profession is bright, as it the only medical profession comprising such vast experience in patient diagnostics. However, laboratory specialists need to develop strategies to provide this expertise, by adopting to the quickly evolving technologies and demands. This opinion paper summarizes some of the disruptive technologies as well as strategies to secure and/or improve the quality of diagnostic patient care and the laboratory specialist profession.

Implementation of Lean Kaizen to Reduce Waiting Time for the Indonesian Health Social Security Agency Prescription Services in Hospital Pharmacy InstallationImplementation of Lean Kaizen to Reduce Waiting Time for the Indonesian Health Social Security Agency Prescription Services in Hospital Pharmacy Installation

BACKGROUND: The second largest Health Social Security Agency (BPJS) outpatients visits in Hospital Pharmacy Installation, Indonesia, has reached 450-600 visits / day between 2017 and 2018. This has an impact on the number of prescriptions to be served. Simultaneous doctor practice schedule and less human resources are the reasons for not achieving the service standard time set by the Ministry of Health (≤60 minutes for concoctions and ≤30 minutes for non-concoctions). AIM: The purpose of this study was to determine the waiting time for BPJS prescription services in the current state and future state after the implementation of lean kaizen through Plan-Do-Check-Act (PDCA) approach at Grha Permata Ibu (GPI) Hospital. METHODS: The research method is operational research with qualitative and quantitative approaches through direct observation and in-depth interviews. RESULTS: The implementation of lean kaizen decreased the lead time from 135.31 minutes to 9.11 minutes in scenario-1 and 7.49 minutes in scenario-2 and a decrease in lead time from 185.17 minutes to 31.09 minutes in scenario-1 and 29,15 minutes in scenario-2 for the concoctions. PDCA in lean kaizen is for use in conditions where waste is most closely related to human behavior, but there is still a waste motion that has not been intervened. CONCLUSION: This study suggests an updating information to prioritize changes in pharmaceutical layout, carry out continuous monitoring, and encourage IT to develop IT-based pharmaceutical services.

Learning from Lean: a quality improvement project using a Lean-based improvement approach to improve discharge for patients with frailty in an acute care hospital

A Lean-based improvement approach was used to complete a quality improvement project (QIP) focused on improving speed and quality of discharge of frail patients on two wards at a large teaching hospital in the UK. This was part of a national initiative to embed continuous improvement within the trust. The aim of the QIP was to improve the proportion of prenoon discharges to 33% of total patients discharged from the ward each day. An 'improvement practice process' followed, which included seven discrete workshops that took the QIP through four distinct phases-understand, design, deliver and sustain. Several improvement methods and tools were used, including value stream mapping and plan-do-study-act (PDSA) cycles. Ten PDSA cycles were implemented across the clinical areas, including improved planning and data collection of discharge, improved communication between nursing and medical staff, and earlier referrals to community hospitals for discharge. Improved performance was identified through the outcome metric prenoon discharges on both wards, with the average increasing from 8% to 24% on ward X and from 9% to 19% on ward Y, with no other significant change seen in other measures. Pettigrew et al's context-content-process change model was used to structure the learning from the QIP, which included the impact of varying ward contexts, the format of conducting improvement with staff, the importance of organisational support, the need for qualitative measures, agreeing to an apposite aim and the power of involving service users. The original aim of 33% prenoon discharges was not achieved, yet there was clear learning from completing the QIP which could contribute to ongoing improvement work. This identified that the Lean-based improvement approach used was effective to some degree for improving discharge processes. Further focus is required on collecting qualitative data to identify the impact on staff, especially related to behaviour and culture change.

Increasing patient flow through neurosurgical critical care: the Leeds Improvement Method

At Leeds General Infirmary, a busy tertiary centre for neurosurgery, there has been little visibility of the step-down status of the patients from intensive care to high dependency or from the latter to a ward bed. The only record of the current situation was limited to the paper notes of the bed managers. Furthermore, accuracy of electronic systems used for staffing levels and bed state were underused. There were gaps in information and furthermore information within the system was unreliable (together defined as ‘defects’). These defects mandated bed managers’ physical presence on each ward to obtain reliable data. This led to unwarranted critical care stays and resultant high rates (up to 40%) of elective operation cancellations requiring a critical care bed.

The Leeds Improvement Method using principles of the Toyota Production System aimed to improve patient flow through critical care and to assess the impact on elective case activity. Problems were identified and changes were implemented over a 1-week period. The changes included measures to reduce time taken for collation of critical bed-state information and improving patient and staffing data quality collected in the electronic patient management system (EPMS) and electronic staffing record (ESR). Impact was monitored for 30 days pre-implementation and post-implementation.

Following intervention, the time taken by the bed manager to gather live bed-state information decreased from 50 to 9 min; the EPMS storing correct bed-state data was improved from 71% to 0% defect; the ESR was improved from 100% to 4% defects; critical care patient step-downs occurring at night (after 20:00) improved from 80% to 20%; and the number of cancelled elective cases over a 30-day period reduced from nine to one.

Implementing these organisational efficiencies can significantly improve critical care patient flow and elective case throughput.

A lean approach to healthcare management using multi criteria decision making

Recent challenges induced by the global pandemic COVID-19 have highlighted the critical importance of coping with a sudden surge in demand for front line healthcare services. Motivated by the success of lean implementation in manufacturing systems, this study attempts to apply the lean principles in healthcare delivery environments. The lean approach begins with the identification of seven types of wastes in any production or service system. This study attempts to identify and prioritize the present in hospitals. The study contributes to the existing body of knowledge in two ways. First, we identify the various sources contributing to the seven basic wastes in healthcare delivery. Second, we prioritize the seven types of wastes and the dimensions contributing to these wastes using a Multi-Criteria Decision Making (MCDM). This paper used the fuzzy analytical hierarchy process approach, which is a well-accepted tool in MCDM. The study was conducted at select hospitals located in and around Pune city in India. We find that waiting, transportation, motion, and defects are dominant in adopting lean practices among the seven wastes. The findings of this study may guide hospital management in strategic planning in adopting a lean healthcare process. To our knowledge, this is one of the first studies to extract, and prioritise lean wastes within the context of the healthcare sector.

Lean readiness within emergency departments: a conceptual framework

Purpose

The purpose of this paper is to develop a framework to assess the lean readiness within emergency departments (EDs) and identify the key quality practices deemed essential for lean system (LS) implementation.

Design/methodology/approach

An extensive review of the lean healthcare literature was conducted, including LS implementation within the healthcare sector (both generally and in EDs), best ED quality practices, essential factors for LS implementation within healthcare and lean readiness assessment frameworks. The authors identified six main categories from a literature review (top management and leadership, human resources, patient relations, supplier relations, processes and continuous improvement (CI)), and validated these based on experts’ opinion.

Findings

Several factors were identified as crucial for EDs, including top management and leadership, human resources, patient relations, supplier relations, processes and CI.

Research limitations/implications

The framework has not yet been tested, which prevents the author from declaring it fit for EDs.

Practical implications

This framework will help ED managers determine the factors that will enable/hinder the implementation of LSs within their premises.

Originality/value

To the author’s knowledge, this is the first lean readiness assessment framework for EDs and one of the few lean readiness assessment frameworks in the literature.

Improving the Workflow Efficiency of An Outpatient Pain Clinic at A Specialized Oncology Center by Implementing Lean Principles

Objective

Adult outpatient oncology pain clinics face many challenges due to the increased number of patients, the restriction of electronic appointment systems, overcrowding, waiting time, and patient dissatisfaction. This project aimed to improve clinic time efficiency, decrease clinic waiting time, and improve patient satisfaction.

Methods

Lean thinking concepts and their tools, for example, value-stream mapping and value added (VA)/non-VA (NVA) analysis were used. Electronic appointment system slots were stratified based on patient visit type. A total of 187 patients were included in a time-motion survey at three different occasions: preintervention (n = 67) and two consecutive quarter postintervention time points (n = 64, n = 56). Simultaneously, patient satisfaction was reported quarterly by a quality management office.

Results

The pain clinic workflow became more efficient; the mean clinic waiting time decreased from 72.5 min at preintervention to 19.5 and 21 min at the two postintervention quarters, respectively. Moreover, patient satisfaction improved from 75% at the preintervention to 100% and 96.7% at the two postintervention quarters.

Conclusions

Redesigning the process of an electronic appointment system using lean thinking considerably decreases patients' waiting time, improves patient satisfaction, improves resource utilization, allows proper scheduling based on patient visit types, eliminates unnecessary waste processes, and reallocates health-care providers' time toward direct, individualized patient care.