Smarter healthcare: Integrating mobile technologies and decision management

Integrating disparate systems to provide timely information at the point of care is a critical aspect of improving patient care in today's healthcare environment. Using the right combination of technologies for integration, mobile applications, and operational intelligence can unlock the potential held within the voluminous information that flows between systems today, ultimately improving quality of care and even patient outcomes. This article shows how the combination of IBM® Integration Bus, IBM Operational Decision Manager, and mobile applications built with IBM Worklight can work together to achieve these goals. This content is part of the IBM Business Process Management Journal.

Curtis Miles (crmiles@ca.ibm.com), Client Technical Professional, IBM

Photo of Curtis Miles Curtis Miles is a technical professional with IBM, helping organizations to understand and apply IBM's MobileFirst portfolio as well as IBM Business Process and Decision Management software to solve their business problems. He has an extensive background in software development, particularly in the area of Java, web technologies, and mobile applications, and especially enjoys creating rich, innovative user experiences. When not busy innovating for IBM, he enjoys relaxing with his family and friends at his home just outside of Toronto.



Ann Ewan (ewan@ca.ibm.com), Senior Information Developer, IBM

Photo of Ann EwanAnn Ewan is a Senior Information Developer with the IBM Business Process Management Information Development team. Her recent projects include the Interactive Installation and Configuration Guide and Interactive Migration Guide that were delivered with IBM Business Process Manager V8.5 to enable customers to generate a set of customized topics. Ann is also a published author of fantasy novels (Firedrake and Brondings' Honour), and had two short stories published in books that came out in 2013.



02 April 2014

Overview

Smarter healthcare involves tapping into the valuable information that is already moving between healthcare systems. It's about adding intelligence and unlocking potential, so that you can use integrated healthcare systems not just for updating systems of record, but also for improving patient outcomes.

You can unlock the potential in smarter healthcare in three ways:

  • Enable mobile device communication to unleash doctors from bedside computers and paper charts, and yet always keep them connected.
  • Detect patterns of events that are happening within the healthcare system to identify where a follow-up is required, without doctors needing to return to the system to check the results.
  • Analyze information according to the healthcare facility's policies, flag potential problems, and aid in detection of abnormal conditions and situations that require human involvement.

To showcase these examples of unlocking potential, let's meet Rachel Wilson, an insurance broker and mother of three, who lives in Greensborough, North Carolina. In this story, Rachel is admitted to the Brook Valley Wellness Center for routine surgery to remove a benign cyst.

The physicians at Brook Valley, including Dr. Karyn Bloomfield, who will be caring for Rachel, are all equipped with tablet mobile devices to provide on-demand access to patient information, test results, and industry research. For example, when Rachel arrives at Brook Valley for her surgery, her doctor is immediately notified through her mobile device.

Rachel is admitted and her doctor is notified

Because Dr. Bloomfield keeps track of the status of all her patients on her mobile device, she is notified that Rachel is now under her care as soon as Rachel is admitted to Brook Valley.

Figure 1. Dr. Bloomfield's mobile app on her tablet, showing her current list of patients
Mobile device with photos and information about Rachel and three other patients at Brook Valley Wellness Center

Dr. Bloomfield receives this notification because Jimmy, the Brook Valley Wellness Center integration developer, is working behind the scenes, empowering the doctors to have access to the latest information in support of their patients' care.

Hospitals like Brook Valley use IBM Integration Bus (hereafter called Integration Bus) and the IBM WebSphere® Message Broker Connectivity Pack for Healthcare to connect to other healthcare facilities for access to patient records, pharmacy records, and so on.

Figure 2. Hospitals connected through IBM Integration Bus
Diagram showing two hospitals connected through IBM Integration Bus with the Connectivity Pack for Healthcare

IBM Integration Bus, formerly known as WebSphere Message Broker, is an enterprise service bus (ESB) that provides connectivity and universal data. It includes policy-based workload management and business rules, and is integrated with IBM Business Process Management (BPM) and Microsoft.NET.

The Connectivity Pack for Healthcare (HCP) provides clinical application and device integration for more-connected healthcare systems. With pre-built connections and patterns, healthcare providers, payers and payees, and life sciences, organizations can rapidly integrate applications and devices that are found within healthcare IT environments. HCP can help improve quality of care and reduce manual errors by exchanging healthcare information with more thoroughness and efficiency.

As is the case with many healthcare organizations, Brook Valley uses the HL7 message specification. As you might already know, HL7 messages are used to transfer electronic data between disparate healthcare systems. In this case, the messages are sent through Integration Bus. By additionally using IBM Operational Decision Manager (ODM) to detect message patterns of interest, Brook Valley can enable operational intelligence within the healthcare infrastructure.

Each HL7 message sends information about a particular event, such as a patient admission. Figure 3 shows the Admit Patient message for Rachel.

Figure 3. ADT^A01 message
Screen capture showing an HL7 Admit Patient message

Each HL7 message contains a series of segments, each of which captures information within a well-defined structure. The second field shows what characters are being used as delimiters. The ninth field gives the code and event, in this case ADT^A01, which is an Admit Patient event. You might also be able to see Rachel's name, her address, next of kin, Rachel's primary care physician, and a lot of other information, including the ward, room, and bed assigned to Rachel, in the Patient Information section.

Behind the scenes, Jimmy, the integration developer, built the connection that notified Dr. Bloomfield when Rachel was admitted. He used the IBM Integration Toolkit to create message flows so that received HL7 messages are sent to all the places that might be interested in doing something with that message. In this case, he augmented a message flow so that admittance messages are sent to the appropriate doctor, resulting in the patient appearing on the doctor's mobile device.

Figure 4 shows a message flow in the IBM Integration Toolkit. Jimmy assembled and linked nodes that receive the message to ensure that it is processed based on its contents and routed to the correct destination.

Figure 4. Part of a message flow in the IBM Integration Toolkit
Screen capture showing a process diagram with part of a message flow, including nodes named FlowOrder, Parse Using MRM, and Message Router Subflow

In this case, Jimmy uses a subflow to isolate the logic for processing the admittance messages so that they can be sent to the doctor’s mobile device. To distribute messages, he takes advantage of the integrated support for Message Queue Telemetry Transport (MQTT), a reliable, low-latency, and low-bandwidth message transfer protocol for sending messages to the mobile device. To ensure that only admittance messages are handled (for now), he adds a filter pattern to determine what message type to look for. He can drill down into the HL7 message to add the filter pattern that he needs.

Figure 5. Hospitals connected through IBM Integration Bus and using mobile applications built with IBM Worklight
Diagram showing two hospitals connected through IBM Integration Bus with the Connectivity Pack for Healthcare. The bus is connected to mobile applications built with IBM Worklight.

Dr. Bloomfield requests tests and detects delayed test results

After the surgery, Rachel unfortunately does not recover quite as quickly as Dr. Bloomfield expected. The doctor orders a Comprehensive Metabolic Panel (CMP), which is a full set of blood tests, to get a better picture of Rachel's condition.

Figure 6 shows the Observation Request (ORM^O01) HL7 message that is sent through the system when Dr. Bloomfield orders Rachel's blood tests.

Figure 6. ORM^O01 message
Screen capture showing an HL7 Observation Request message.

Behind the scenes, Jimmy added some intelligence to detect when test results have been delayed longer than expected. Earlier, he augmented the message flow in the IBM Integration Toolkit so that notifications are sent to the doctors' mobile devices based on the type of message. He can also use the IBM Integration Toolkit to integrate with IBM ODM to identify patterns of events so that the doctor can notice something that was expected but didn't happen. To accomplish this integration, Jimmy added another subflow to detect overdue tests. When a test request message flows through the system, it is routed to the DetectOverdueTests subflow.

Figure 7 shows the updated message flow with the path added for test request messages (ORM^O01) to follow, and the condition under which messages will follow that path.

Figure 7. Adding the DetectOverdueTests subflow in the IBM Integration Toolkit
Screen capture showing the message router flow with five nodes: Input, Route Message, Prepare Notification, PublishWithMQTT, and Output. A DetectOverdueTests subflow has been added after Route Message, and Route Message has a filter pattern to detect ORM^O01 messages.

The DetectOverdueTests subflow sends an event to the decision management component, which detects patterns and sends a notification when a pattern is matched. Specifically, in this case, it is looking for the pattern of a test request message that is being sent out without a corresponding test result message being sent back within a reasonable amount of time. This flow identifies tests that are overdue or are taking longer than expected.

Figure 8. Hospitals connected through IBM Integration Bus, using mobile applications and communicating with ODM through business events
Diagram showing two hospitals connected through IBM Integration Bus with the Connectivity Pack for Healthcare. The bus is connected to mobile applications built with IBM Worklight and to IBM ODM through business events.

Again, this subflow provides information to Dr. Bloomfield that she did not have access to before. When the test results are overdue, she receives a notification on her mobile device. She no longer has to remember to repeatedly check for the test results because the system automatically detects patterns of events and notifies her when a follow-up is required.

Figure 9. Notification that test results are overdue
Mobile device with notification that Rachel's test results are overdue and three possible actions to take: Forward to Circle of Care, Contact laboratory, and Wait an additional period of time (30 minutes).

When she receives the notification that Rachel’s tests are taking longer than expected, Dr. Bloomfield is offered several actions to take. She decides to notify someone in Rachel's Circle of Care, which is the set of physicians and other professionals who are involved in caring for Rachel. She clicks Circle of Care and selects Billy, who is one of the clerks in Rachel's Circle of Care, so that he can follow up on the overdue test results.

Because Jimmy used the MQTT protocol to enable the communication between Dr. Bloomfield and Billy, the message delivery is guaranteed and Dr. Bloomfield can immediately see that Billy has received her message.

Figure 10. Rachel's Circle of Care
Mobile device with Rachel's photo in the middle and eight other photos around her. One of them is Dr. Karyn Bloomfield and another is Billy, whose name is selected.

Business rules provide decision support for test results

Shortly after Billy follows up with the laboratory, Dr. Bloomfield receives Rachel's test results.

Figure 11 shows the Observation Result (ORU^R01) HL7 message that contains the results of Rachel's blood tests. This message has a lot of complicated information. Figure 11 also shows a portion of the much larger message.

Figure 11. ORU^R01 message
Screen capture showing part of an HL7 Observation Result message.

In the IBM Integration Toolkit, in addition to the intelligence that Jimmy added regarding event patterns of interest, Jimmy also integrated business rules so that, for example, returning test results are flagged if they might be abnormal. Abnormalities are found by comparing the expected values of the test components with the actual values that are identified in the tests. This information can then be presented to the physicians to be used in their diagnosis and treatment decisions.

To enable this integration, Jimmy has added an AnalyzeResultsWithBusinessRules subflow for returned results to the message flow. This subflow uses a map to move the information from the HL7 results message into a format that the business rules engine can read. The results then run through the business rules engine (using a web service call) to flag anything that Dr. Bloomfield should be aware of when she reviews Rachel's test results.

In addition to detecting whether levels are outside the expected range, you can also use business rules to identify whether combinations of levels might together indicate something that needs to be investigated.

Figure 12 shows the same flow as before, but now updated to include an additional path for test result messages (ORU^R01) to follow, where they will be routed through the business rule management system.

Figure 12. Adding the AnalyzeResultsWithBusinessRules subflow in the IBM Integration Toolkit
Screen capture showing the same flow, with an additional AnalyzeResultsWithBusinessRules node added after Route Message, and an additional filter pattern added to Route Message to detect ORU^R01 messages.

Figure 13 shows the subflow. The Extract Data from Test Result activity is the map that moves the information from the message into a format that the business rules engine can read. Because the rules engine is called as a web service, Jimmy needs a SOAP envelope to contain the message. Then the rules engine is called again and the information is formatted again on the way back.

Figure 13. Inside the AnalyzeResultsWithBusinessRules subflow
Screen capture showing a process with the following nodes: Input, Extract Data from Test Result, SOAP Envelope, Analyze Results with Business Rules, and Prepare Test Result Message.

The notification appears on Dr. Bloomfield’s mobile device, noting some potentially abnormal conditions and things that she might want to follow up on flagged in red text.

Figure 14. Rachel's test results
Mobile device showing that Rachel's test results have arrived with three warnings: Combination of low calcium and high potassium indicates a risk factor for Disease A. Calcium (Mass/volume) in Serum or Plasma: Below normal levels. Potassium (Moles/volume) in Serum or Plasma: Above normal levels. There are two possible actions to take: Open Results or Forward to Circle of Care.

Dr. Bloomfield can see that Rachel’s calcium level is lower than expected, the potassium level is higher than expected, and that the combined result might be a risk factor for a specific condition or some specific conditions.

Figure 15. Hospitals connected through IBM Integration Bus, using mobile applications, communicating with ODM through business events and business rules
Diagram showing two hospitals connected through IBM Integration Bus with the Connectivity Pack for Healthcare. The bus is connected to mobile applications built with IBM Worklight and to IBM ODM through business events and business rules.

To see why that result was returned, let's look at the business rules within the Decision Center, the web-based environment within IBM ODM that allows non-IT subject matter experts to create business rules and keep them up-to-date as new policy changes are needed. For each of the factors in the test, Brook Valley Wellness Center identified the expected ranges and abnormal thresholds, based on various factors, and captured this information within a decision table. When an abnormal value is identified, the specific message is presented to the doctor.

Figure 16 shows the Blood Test Risk Evaluation decision table.

Figure 16. Blood Test Risk Evaluation decision table in the IBM Decision Center
Screen capture of IBM Decision Center showing Smart Folders, Business Rules > Individual Factors, and the Decision Table preview. The Decision Table has columns for: Factors (such as Calcium), Quantity (minimum and maximum), Units, Reading Level, and Messages that will be displayed.

Click to see larger image

Figure 16. Blood Test Risk Evaluation decision table in the IBM Decision Center

Screen capture of IBM Decision Center showing Smart Folders, Business Rules > Individual Factors, and the Decision Table preview. The Decision Table has columns for: Factors (such as Calcium), Quantity (minimum and maximum), Units, Reading Level, and Messages that will be displayed.

Brook Valley has also added some business rules that evaluate combinations of factors. In Figure 17, a business rule named Risk of disease A shows how a combination of factors might be evaluated. If both the calcium level is low and the potassium level is high, a high severity notification is sent to the doctor's mobile device. This business rule is the one that was evaluated by the integration platform as Rachel's results came in.

Figure 17. Business rule in the IBM Decision Center
Screen capture of IBM Decision Center showing a business rules that reads: if all of the following conditions are true: - the reading level of 'Calcium' in 'test results' is 'Low' - the reading level of 'Potassium' in 'test results' is 'High', then add a note to 'risk findings' with 'High' severity and message 'Combination of low calcium and high potassium indicates a risk factor for Disease A';

Click to see larger image

Figure 17. Business rule in the IBM Decision Center

Screen capture of IBM Decision Center showing a business rules that reads: if all of the following conditions are true: - the reading level of 'Calcium' in 'test results' is 'Low' - the reading level of 'Potassium' in 'test results' is 'High', then add a note to 'risk findings' with 'High' severity and message 'Combination of low calcium and high potassium indicates a risk factor for Disease A';

The messages that Dr. Bloomfield sees bring abnormal results to her attention and gives her valuable decision support as she reviews Rachel's results and decides how to proceed. She looks at the results in more detail and decides to notify other people in Rachel's Circle of Care.


Rachel is discharged

Fortunately, after Rachel had a good night's sleep, her test results returned to normal. She was kept in Brook Valley for an extra day, but then she was well enough to be discharged.

The Discharge Patient (ADT^A03) message (Figure 18) flowed through the integration platform.

Figure 18. ADT^A03 message
Screen capture showing an HL7 Discharge Patient message

Thanks to Jimmy's hard work earlier, the message was sent to Dr. Bloomfield’s mobile device, and Rachel was removed from the list of patients under her care.


Conclusion

In this article, you learned three ways to unlock the potential in smarter healthcare:

  • Enable mobile-device communication to free doctors from their bedside computers so that they can always be connected. Dr. Bloomfield received notifications and updates as Rachel was admitted. She requested tests by using her mobile device and later received the results through her mobile device.
  • Detect patterns of events that are happening within the healthcare system to identify where a follow-up is required. For example, with smarter healthcare the doctor doesn't need to physically return to the system to determine whether diagnostic test results are available. Dr. Bloomfield was notified when receiving the test results took longer than expected, and she could follow up immediately. She was also able to immediately contact and communicate with the other healthcare professionals who were involved in Rachel's care.
  • Analyze information according to the healthcare facility's policies, flag potential problems, and aid in detection of abnormal conditions and situations that require human involvement. Dr. Bloomfield saw that some of the test results were abnormal and was able to follow up immediately.

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