Hospitals (14)

A quick and simple set of steps to guide a data breach are in a slideshow in Health Data Management. The overall goals are to plan to minimise damage and react constructively. It suggests that the day after, hospitals should react, respond, transform and sustain. Advice is don’t report a breach until you understand where it’s from, what its goal was and which part of the infrastructure was compromised.

The four steps include:

  1. React: understand what happened and collect and preserve evidence, detect a stop further damage and look for previous, unidentified attacks
  2. Respond: deal with security gaps and concerns and add new, effective defences
  3. Improve: develop organisation’s cyber-security priority, attitude and behaviour  and redefine security
  4. Sustain: monitor constantly and support a dedicated security team led by a Chief Information Security Officer.

It’s a simple checklist that CEOs and managers in Africa’s hospital and other health services can keep in their draw labelled What to do in the Event of a Security Breach.

A number of hospitals in the Steve Biko Academic Cluster in Tshwane, Gauteng, South Africa have reduced patient waiting times by at least two hours using a new Electronic Bed Management System (eBMS). It was introduced by the provisional Department of Health as part of their healthcare modernisation initiative. Gauteng hospitals manage an average of 27.7 million patients annually, and often experience bed shortages. To address this, the department piloted eBMS at Steve Biko Academic Hospital earlier this year, says an article in allAfrica.

The eBMS allows medical staff to identify the location of available beds, so improve patient movement and management. Using innovative cloud-based technology, eBMS hospital staff can view current bed availability in their hospital on large display screens or over the Internet on their mobile device or PC. 

The pilot phase was successful, so a decision was made to roll it out across the cluster., and hospital staff and management have commended eBMS. Hospital personnel use the information to make both long and short term decisions that have led shorter patient waiting times and better ward and staff utilisation. 

"Management is using eBMS to make reallocation decisions about ward beds. We have split not frequently used wards to move beds to a ward that is always busy. Having had the system in place since early January, Kalafong has already seen a two hour reduction in their casualty waiting times," said Dr Htwe, clinical manager at Kalafong Hospital. "We are so excited about being able to view available beds across the cluster. This increases transparency which helps us improve patient care," added Dr Htwe.

The Gauteng Emergency Medical Services uses eBMS too, including when transporting patients to hospitals. It leads to better coordination of it services.

MEC for Health, Ms Qedani Mahlangu said introducing BMS was part of initiatives by the Department to use technology to improve healthcare. She anticipates that eBMSwill be fully implemented across the province by the end of 2016.

As eHealth amasses more and more data, it should drive healthcare managers to use it to help to improve healthcare performance and patient outcomes. It’s not a simple step. Pyramids with data as a foundation build up with information, then knowledge, then may branch into wisdom or power, assuming the two may not be related. Robert Staughton Lynd, a sociologist and co-author of Middletown, had view on this. It was “Knowledge is power only if man knows what facts not to bother with.”

Warren Strauss’s director of the Advanced Analytics and Health Research Resource Group at Battelle, an innovation company. He says many USA hospitals find it difficult to cut through the clutter of data required by the Centers for Medicare & Medicaid Services (CMS) and Centers for Disease Control and Prevention reporting to Agency for Healthcare Research and Quality (AHRQ) indicators.

The challenge is how can hospitals use this data for initiatives that can improve and predict performance and improve patient outcomes? There are crucial goals for eHealth in Africa too. They determine many of eHealth’s benefits. His solution’s set out in Hospital Impact. It includes:

  • Reviewing patterns in adverse events so hospitals can determine where they need to focus quality improvement efforts and track results of their efforts over time
  • Don’t miss opportunities for improvement because hospitals have a long way to go to realise the full potential of data-driven improvement
  • Present data that’s timely and in a format that’s usable for decision makers, a considerable challenge for many hospitals
  • Make sure that quality information:
    • Is easy to find, use and understand
    • Provides actionable insights using effective analytics tools
    • Offer time-to-value, so it’s as close to real time as possible
    • It’s accessible and flexible
    • Offers benchmarking and collaboration
    • Is secure.

Achieving these needs a combination of factors to be in place. They’re the right approach, resources and leadership in using analytics. It also needs rigour in selecting relevant data, an artistic skill identified Charles de Lint, a writer; “The best artists know what to leave out.”

A critical theme in South Africa’s National Health Insurance initiative is changing the way that hospital services are financed. Switching to Diagnosis Related Groups (DRG) was part of the original Green Paper in 2011, and remains firmly in place in the updated version released in December 2015. It’s not a simple switch, and needs several building blocks to work effectively.

It’s also a proven financing methodology. Many countries use DRGs, and have developed their bespoke versions. Originally designed as a quality assurance tool in the USA, DRGs were used by the Medicare Program to reimburse hospitals using prospective prices from 1983. It had 470 DRGs across 23 Major Diagnostic Categories (MDC), each of which can include both surgical and medical services. As combinations of WHO’s International Classification of Diseases (ICD), currently ICD-10, they initially didn’t reflect the severity or complexity of the conditions, and adjustments for these came later. There are now over 750 DRGs in use for 26 MDCs, depending on the version and country. Some DRG prices distinguish day case from inpatients. 

About half the DRGs are medical, 47% for surgery in MDCs, plus 2% for pre-surgery and 1% for surgical cases not in MDCs and two DRGs for an invalid principal diagnosis and ungroupable workload. About 3% of DRGs aren’t in an MDC. It’s roughly a 50:50 split between medical and surgical cases.

Prospective DRG prices are seen as a way to control healthcare costs. It requires hospitals to manage their unit costs and annual expenditure within these. So why’s it not that simple? DRGs don’t extend across all hospital activity and need time to catch up with new medical techniques. These can either have their own reimbursement rates, or take so long to set that they can be a brake on investment, a claim for a slow take up of telemedicine. A recent example’s the proposal in 2015 from the American Association of Hip and Knee Surgeons (AAHKS) to the Centers for Medicare & Medicaid Services (CMS) to modify or establish a new MS-DRG for total hip arthroplasty (THA) cases involving patients with hip fracture. For 2016, CMS has released DRG version 33, one a year since 1983, including eight changes to existing Medicare Severity (MS) DRGs.

Since 1983, DRGs have almost gone global. As a proven reimbursement method, many countries have refined them to match their health systems. The UK and Ireland have Healthcare Related Groups (HRG). The Nordic countries have their NordDRG. These reflect health systems’ bespoke characteristics. This includes their different healthcare models and their specific cost structures. 

Setting prices for DRGs often starts with existing unit costs. This needs a costing system with two main methodologies. One’s Total Absorption Costing (TAC) to allocate and apportion expenditure to unit costs. The others a variable and semi-variable costing methodology to identify expenditure changes arising from changes in workloads. Semi-variable costs are the most challenging to compile. Both methodologies rely on sound workload data and ICD 10 coding.

Using TAC throws up a specific challenge for healthcare. Direct costs that can be allocated to specific patients are a small proportion of total costs, maybe less than 10%. This creates the risk of large skewed unit costs derived mainly from using formulae for apportionments. One way to minimise these inconsistencies it to create cost pools for the MDCs or specialties where direct costs can be increased significantly. Each MDC can have its own apportionments on to workloads. This minimises skewed results, but doesn’t avoid them, so comparisons between hospitals’ will still reveal some odd numbers.

For South Africa’s switch to DRGs, some important activities are needed that apply lessons learned from other health systems. They include:

  • Test the accuracy and completeness of hospital’s workload data, including duplication challenges as reported in eHNA, and minimising the workload assigned to DRGs for invalid principal diagnoses as discharge diagnoses and ungroupable workload
  • Test hospitals’ ICD-10 readiness to avoid the USA’s unhappy experiences reported in eHNA
  • Design the DRG grouper needed for reimbursement, including DRG choices for patients with more than one DRG for their hospital stay
  • Set up reimbursement models for hospital services that don’t fit the DRG model
  • Design and trial a national hospital costing methodology that reconciles workloads x unit costs to total expenditure
  • Refine the costing methodology
  • Run a parallel DRG financing model alongside the existing model to identify large swings
  • Design an interim DRG financing model to minimise financial risks
  • Estimate and monitor changes to hospital costs and the impact on total health service spending and finance.

With the NHI’s fourteen-year development timescale, now’s the time to start these. An early start provides time to iron out data idiosyncrasies and will help to minimise risks, which are always prevalent when financing models change. eHNA’s looking forward to reviewing these soon.

As African countries develop and apply their interoperability (IOp) plans, there’s a salutary lesson from USA hospital. A survey by Himagine Solutions, a coding company, says hospital productivity dropped after hospitals switched to WHO’s International Classification of Diseases and Related Health Problems Series 10 (ICD-10) the standard diagnostic tool for monitoring the incidence and prevalence of diseases and other health problems. It’s also an essential data source for Diagnosis Related Groups (DRG) and their derivatives.

ICD-10’s a statistical tool requiring compliance with WHO’s definitions and rules. The data enables accurate, consistent and comprehensive capture of data for secondary purposes, including billing. It’s not designed for recording by clinicians at points of care. That’s Systematized Nomenclature of Medicine--Clinical Terms’ (SNOMED CT) role.

Himagine’s survey and Benchmark Report says 75% of respondents predicted the adverse productivity impact from ICD-10 is more than 30%.

Large hospitals, those with more than 250 beds, reported a productivity drop of 30 to 45% for inpatient services, and a 20 to 40% drop for outpatient services. Community hospitals, which have fewer than 250 beds, the inpatient productivity drop was 22 to 33%, and 35 to 40% for outpatients. 

Large Hospitals (over 250 beds) are seeing a 30-45% reduction on the Inpatient side and a 20-40% reduction on the Outpatient side. When it comes to Community Hospitals (under 250 beds), the Inpatients reductions are much lower ranging in a productivity decline of 22-33% while the outpatient is higher on average hovering around 35-40%. Teaching hospitals reported an average 40% drop in inpatient productivity, with a 10 to 35% ranges for outpatients.

It may be that part of the explanation may be an increased rejection of reimbursement claims due to incomplete supporting data, so a loss of income, rather than a productivity drop. Either way, it’s a considerable disruption to hospital’s operational and financial performance. Part of the solution may be better and more training for coders and billing teams and greater use of eHealth solutions. Another part is the low use of Computer Assisted Coding (CAC), about 56%. This could increase to 75% in a year’s time.

As Africa’s health systems move their eHealth on and rely more on health insurance schemes, it’s important they don’t have a similar experience to the USA. eHealth can be disruptive. It’s a bad idea for hospitals to have strained productivity and income too.

A doctor working in England’s emergency services has developed a wide-ranging EHR that’s available for African countries. Dr Michael Brooks set up PatientSource. It has several modules developed by clinicians: 

  • eCase notes
  • ePrescribing
  • eObservations
  • Investigations as Computerised Physician Order Entry (CPOE)
  • Patient administration
  • eDischarge
  • Diabetes management
  • Bespoke specialty modules
  • Community mHealth
  • Interoperability (IOp) tools
  • Auditing 

These enable benefits of:

  • Better responses to abnormal vital signs
  • Improved patient safety
  • Health workers’ time savings
  • User friendly for nurses
  • Better monitoring for patient outliers.

PatientSource works on tablets, so clinicians can take their eHealth to bedsides. Because it’s built around doctors and nurses working with their patients, and it has several IOp tools, it can fit into most healthcare settings in different health systems. It offers an effective EHR option for Africa’s hospitals.

Analytics for better healthcare’s a growing trend that Africa’s citizens and health systems can benefit from. A post in Healthcare Informatics describes how Seattle Children’s Hospital’s setting a pace. It has a Data and Analytics Team that ‘s been focusing on leveraging large and complex data to improve patient safety and outcomes, reduce costs, broaden population health access and experience, and drive innovation. The hospital’s been using analytics since 2007, so has a track record.

The team’s leader, Dr Eugene Kolker, calls it “Data-informed decision-making, and execution.” It’s part of the hospital’s Benchmarking Improvement Program that uses clinical outcomes measures to benchmark our clinical performance against the best-performing patient care organisations.

The operational model has two main steps. First, the team uses data and analytics to provides service teams with comprehensive and unbiased views of their current situation to help them to identify where they can improve. Second, the team integrates that information with service teams’ institutional knowledge and insight to develop specific recommendations and effective strategies for change.

The team links into the US News and World Report (USWNR), a USA media company, for hospital ranking data, and the National Association of Children’s Hospitals and Related Institution’s (NACHRI) database. The objective is to identify areas of improvement, opportunity and business growth, the set priorities for action. Examples of the teams benchmarking successes are hospital-acquired infections, lowering the rate of unintended removal of breathing tubes, breast milk management, and in minimising 30-day readmissions.

For Africa’s health systems to adopt a similar approach, they’ll need access to benchmarks. These aren’t readily available. They’ll also need a team of skilled analysts. Both of these need investment. Hospitals can begin with teams that can help health workers improve clinical services.

The team at the African Centre for eHealth Excellence (Acfee) sees eHealth as a combination of people, health ICT and healthcare transformation. It can also include time as the temporal dimension needed for them to interact. Computer World Hong Kong has a valuable case study that shows how these themes play out, and keep playing out.

It’s from the Hong Kong Hospital Authority (HKHA). In the early 1990s it computerised its clinical operations and healthcare services. In 1995, it launched phase 1 of its clinical management system (CMS) for clinical documentation and order entry. Now, all HKHA’s public hospitals run CMS 3.0, and doctors use it to read investigation results, enter medical records and place clinical orders. Since 2006, it uses Public-Private Interface-electronic patient’s Record (PPI-ePR) to share patient’s records between Hong Kong’s 42 public and 11 private hospitals.

Doctors use PPI-ePR to read patients’ essential health data, including problems, diagnoses summary, laboratory summaries, encounter summaries, allergies, adverse drug reaction and summaries of prescribing histories. Privacy’s regulated by the Personal Data (Privacy) Ordinance (PDPO) and specific EHR legislation. An EHR Commissioner can issue codes of practice and guidelines. A goal is to instil public confidence in the system.

An important eHealth objective is making hospitals safer. The Prince of Wales Hospital (PWH) audited the top errors in its medication process in 2011-2013. Findings suggested that almost half the medical incidents involved wrong drug prescriptions. This helps to design and target the actions needed to improve. Automating drug order placement is part of the response.

In 2014, PWH implemented the In-Patient Medication Order Entry (IPMOE) system. It’s tried different mobiles with different user groups and used desktops for doctors to review medications at patients’ bedsides in real time. Nurses use a 2D barcodes from prescribed drugs packs to transfer data to IPMOE. They’re alerted when patients have an updated drug prescription, when they need to take their prescribed medicine and when some drugs are reaching the end of their usable life. The IPMOE system led to big workflow changes. It’s now in 40% of PWH’s wards and has planned roll out to two other hospitals by 2017 and the rest by 2018.

In 2011, the Adventist Hospital implemented a CMS to help nurses to administer the right drugs for the right patient. Since then, it’s launched a CMS mobile application that supports nurses in 22 clinical functions. Some aren’t used, like the Operation Theatre Time-Out OTTO) function. Nurses said it needed too much time.

HKHA’s story’s about people, health ICT, healthcare transformation and time. It shows how eHealth’s a bit like history, just one thing after another, and no end to it. Each country has to follow its own sequence. HKHA has offered illuminating sign posts for the long eHealth journey.

Traditionally, telemedicine is associated with consultations over considerable distances and often with the equivalent of a preliminary to an outpatient appointment.  There’s a growing case for telemedicine driven from ICU. A research study, by Marshall University in West Virginia, USA, published by Telemedicine and e-Health.

ICU telemedicine needs are different. They have higher implementation costs than outpatient versions, but hospitals could benefit more, both for healthcare quality gains and financially, the research says. Quality benefits included better patient safety and patient satisfaction. Teamwork, supervision and communications between health workers improved too.

Affordability of ICU telemedicine may be a challenge, but the costs and benefits are worth exploring, especially for large tertiary hospitals. As they realise benefits, they provide evidence for the case for further expansion, or no expansion.

Apple HealtKit, was officially launched in June 2014. It’s spreading quickly among major US hospitals. A report by Reuters says Apple’s healthcare technology is showing early promise as a way for doctors to monitor patients remotely and cut costs.

Reuters contacted 23 top hospitals in the country, 14 of which were rolling out the pilot program of Apple’s HealthKit services. Apple’s service acts as a repository for health information generated by patients, like their blood pressure, weight and heart rate.

Apple’s HealthKit works by gathering data from sources such as glucose measurement tools, food and exercise-tracking apps and Wi-fi connected scales. The company’s Apple Watch, due for release in April, promises to add to the range of data, which with patients’ consent can be sent to an EMR for doctors to view.

The pilots aim to help physicians monitor patients with chronic conditions such as diabetes and hypertension. HealthKit holds the promise of allowing doctors to watch for early signs of trouble and intervene before a medical problem becomes critical. It could help hospitals avoid admissions and repeat admissions, lowering cost and providing a better service for patients.

Whether or not this services makes its way to African countries remains to be seen. While Africa has seen an uptake in iPhones, many hospitals don’t have the infrastructure or the manpower to maintain such a system.