Lightning Bolt invests heavily in research and software development to solve complex problems in the area of medical staff scheduling.
Lightning Bolt is the leading provider of automated physician scheduling for hospitals around the world. The company manages more than 3 million physician hours each month, helping to create shift schedules that promote work-life balance, productivity and patient safety.
Lightning Bolt’s cloud-based scheduling platform helps hospitals create dynamic staff schedules with a few clicks, automatically optimizing hundreds of complex scheduling rules. Physicians are able to request time-off and shift changes through the platform, creating transparency and a fair system that balances staff needs. The system also includes HIPAA-compliant messaging and detailed analytics.
Working as a staff scientist at the Los Alamos National Laboratory to schedule massively parallel supercomputers in 1998, Lightning Bolt founder Suvas Vajracharya, Ph.D. was approached by a high school friend, a doctor, for help with a big frustration. The doctor noticed that the seemingly simple task of creating call schedules for his group was deceptively complex, time consuming, and often proved an inaccurate science where equitable distribution of staffing resources, or the honoring of individual physician requests, would often conflict or simply could not be met.
Suvas saw that his own technology experience with scheduling supercomputers could provide the foundation for creating an elegant, easy to use solution to solve the inherent complexities in medical staff scheduling. Both supercomputing and medical staff scheduling share fundamental requirements, including the need to distribute tasks equally and efficiently in the presence of complex and often changing rules with varying priorities. Within a few months, Suvas developed a prototype scheduling system to tackle his friend’s challenging problem and Lightning Bolt was born.
The company’s growth has largely been through word-of-mouth between physician executives and hospital operations leaders who have discovered the software and become loyal customers. Lightning Bolt also attends several industry events each year, including HIMSS, MGMA and RSNA.
The vast majority of physician scheduling is still done manually today at America’s 5,700 hospitals. There are emerging players in the space of automated scheduling but nowhere near as established as Lightning Bolt. The company is part of a growing sector of hospital operations technology, including companies such as Silversheet, Modio Health, HealthLoop and AnalyticsMD.
How does your company differentiate itself from the competition
Lightning Bolt is the only platform that considers significant and complex relationships to auto-generate the best possible schedules for large medical organizations. Also, they are the only scheduling system that provides transparency across a healthcare workforce. Since manual scheduling using spreadsheets or paper is the largest competitor, Lightning Bolt’s biggest differentiators tend to be time and efficiency. In one case study, iNDIGO Health Partners generated a $38M ROI over 5 years by switching from manual to automated scheduling with Lightning Bolt.
Guest post by: Sai Subramaniam, Ph.D., Business Head, Life Sciences & Healthcare at Persistent Systems
According to a recent report only 16 percent of hospitals have clinical decision support capabilities, but IT leaders call it a top priority for the next 12 months. Healthcare reform is all about achieving better quality care at lower costs, and clinical analytics is integral in delivering on this promise. For example, reducing 30-day r-eadmissions and hospital-acquired infections alone is expected to save more than $25 billion dollars in the healthcare system. Analytics on integrated claims and clinical data will allow health systems to pinpoint effective clinical and operational interventions. Here are five high-impact outcomes that health systems can achieve using clinical analytics.
30-day Re-admission Avoidance: Hospital re-admission rates are high for patients whether they are in Medicare, Medicaid or Private insurance plans. People with multiple chronic conditions and mental health conditions are at an increased risk of re-hospitalization because of inadequate care at discharge. Demographic and social factors also dictate if the care transition will be effective or not. Evidence-based rules allow stratification of patients based on these factors. This allows caregivers to give more attention to high-risk patients during hospital discharge.
Enhanced Surveillance and Preventive Care: Growing evidence suggests that education and health coaching will facilitate behavior change and achieve cost savings. The population in the program needs to be screened and stratified to identify at-risk patients. Predictive modeling and business rules can help to identify individuals who may not be diagnosed but have relatively high risk of developing diabetes in the future. Similarly, a cancer surveillance model based on linking environmental, genetic, and lifestyle factors can be used. This will allow early interventions and proactive follow-up care.
Improved Medication Adherence: Non-adherence is said to be responsible for more than 10 percent of hospital admissions and 40 percent of nursing home admissions. Patients on average don’t fill more than 25 percent of new prescriptions. Costs because of lack of medication adherence exceeds $100 billion. Predictive analytics on patients’ past prescription claims data will allow the health system to create an adherence score, and facilitate a proactive approach to managing compliance.
Unplanned Admission Avoidance: It’s important for health systems to identify patients with chronic conditions who may be at risk of emergency hospitalizations. For example, studies suggest that people with respiratory and cardiac comorbidities, with higher hospital utilization in prior years, have a higher probability of hospital admission. Determination of such factors along with socio-demographic characteristics, will allow application of predictive models to identify people at-risk.
Length of Stay Performance Management: Several factors impact the patient’s length of stay in the hospital. This includes demographic as well as hospital operational characteristics. There are standards for length of stay based on diagnosis related group and clinical disease factors. By comparing this with patient profiles, providers can utilize resources efficiently to provide optimal patient care. This will result in significant cost savings as better case management should help to reduce the average length of stay.
Dr. Sai Subramaniam is the Vice-President of Persistent Systems’ Life Sciences & Healthcare business. In this role, Sai is responsible for the overall business growth of Healthcare & Life Sciences business segments.