Improving the quality of mental health and substance use treatment: how can Informatics help?

According to the 2012 National Survey on Drug Use and Health, an estimated 43.7 million adults aged 18 or older in the United States had mental illness in the past year. This represents 18.6 percent of all adults in this country. Among those 43.7 million adults, 19.2 percent (8.4 million adults) met criteria for a substance use disorder (i.e., illicit drug or alcohol dependence or abuse). In 2012, an estimated 9.0 million adults (3.9 percent) aged 18 or older had serious thoughts of suicide in the past year.

Mental health and substance use are often associated with other issues such as:

• Co-morbidity involving other chronic diseases like HIV, hepatitis, diabetes, and cardiovascular disease.


• Overdose and emergency care utilization.


• Social issues like incarceration, violence, homelessness, and unemployment.

It is now well established that addiction is a chronic disease of the brain and should be treated as such from a health and social policy standpoint.

The regulatory framework

• The Affordable Care Act (ACA) requires non-grandfathered health plans in the individual and small group markets to provide essential health benefits (EHBs) including mental health and substance use disorder benefits.  


• Starting in 2014, insurers can no longer deny coverage because of a pre-existing mental health condition.


• The ACA requires health plans to cover recommended evidence-based prevention and screening services including depression screening for adults and adolescents and behavioral assessments for children.


• On November 8, 2013, HHS and the Departments of Labor and Treasury released the final rules implementing the Paul Wellstone and Pete Domenici Mental Health Parity and Addiction Equity Act of 2008 (MHPAEA). 


• Not all behavioral health specialists are eligible to the Meaningful Use EHR Incentive program created by the Health Information Technology for Economic and Clinical Health Act (HITECH) of 2009.

 

Implementing Clinical Practice Guidelines (CPGs) with Clinical Decision Support (CDS) systems

 

Clinical Decision Support (CDS) can help address key challenges in mental health and substance use treatment such as:

• Shortages and high turnover in the addiction treatment workforce.


• Insufficient or lack of adequate clinician education in mental health and addiction medicine.


• Lack of implementation of available evidence-based clinical practice guideline (CPGs) in mental health and addiction medicine.

For example, there are a number of scientifically validated CPGs for the Medication Assisted Treatment (MAT) of opioid addiction using methadone or buprenorphine. These evidence-based CPGs can be translated into executable CDS rules using business rule engines. These executable clinical rules should also be seamlessly integrated with clinical workflows.

The complexity and costs inherent in capturing the medical knowledge in clinical guidelines and translating that knowledge into executable code remains an impediment to the widespread adoption of CDS software. Therefore, there is a need for standards that facilitate the sharing and interchange of CDS knowledge artifacts and executable clinical guidelines. The ONC Health eDecision Initiative has published specifications to support the interoperability of CDS knowledge artifacts and services.

Ontologies as knowledge representation formalism are well suited for modeling complex medical knowledge and can facilitate reasoning during the automated execution of clinical guidelines based on patient data at the point of care.

The typical Clinical Practice Guideline (CPG) is 50 to 150 pages long. Clinical Decision Support (CDS) should also include other forms of cognitive aid such as Electronic Checklists, Data Visualization, Order Sets, and Infobuttons.

The issues of human factors and usability of CDS systems as well as CDS integration with clinical workflows have been the subject of many research projects in healthcare informatics. The challenge is to be bring these research findings into the practice of developing clinical systems software.

Learning from Data

Learning what works and what does not work in clinical practice is important for building a learning health system. This can be achieved by incorporating the results of Comparative Effectiveness Research (CER) and Patient-Centered Outcome Research (PCOR) into CDS systems. Increasingly, outcomes research will be performed using observational studies (based on real world clinical data) which are recognized as complementary to randomized control trials (RCTs). For example, CER and PCOR can help answer questions about the comparative effectiveness of pharmacological and  psychotherapeutic interventions in mental health and substance abuse treatment. This is a form of Practice-Based Evidence (PBE) that is necessary to close the evidence loop.

Three factors are contributing to the availability of massive amounts of clinical data: the rising adoption of EHRs by providers (thanks in part to the Meaningful Use incentive program), medical devices (including those used by patients outside of healthcare facilities), and medical knowledge (for example in the form of medical research literature). Massively parallel  computing platforms such as Apache Hadoop or Apache Spark can process humongous amounts of data (including in real time) to obtain actionable insights for effective clinical decision making.

The use of predictive modeling for personalized medicine (based on statistical computing and machine learning techniques) is becoming a common practice in healthcare delivery as well. These models can predict the health risk of patients (for pro-active care) based on their individual health profiles and can also help predict which treatments are more likely to lead to positive outcomes.

Embedding Visual Analytics capabilities into CDS systems can help clinicians obtain deep insight for effective understanding, reasoning, and decision making through the visual exploration of massive, complex, and often ambiguous data. For example, Visual Analytics can help in comparing different interventions and care pathways and their respective clinical outcomes for a patient or population of patients over a certain period of time through the vivid showing of causes, variables, comparisons, and explanations.

Genomics of Addiction and Personalized Medicine

Advances in genomics and pharmacogenomics are helping researchers understand treatment response variability among patients in addiction treatment. Clinical Decision Support (CDS) systems can also be used to provide cognitive support to clinicians in providing genetically guided treatment interventions.

Quality Measurement for Mental Health and Substance Use Treatment

An important implication of the shift from a fee-for-service to a value-based healthcare delivery model is that existing process measures and the regulatory requirements to report them are no longer sufficient.

Patient-reported outcomes (PROs) and patient-centered measures include essential metrics such as mortality, functional status, time to recovery, severity of side effects, and remission (depression remission at six and twelve months). These measures should take into account the values, goals, and wishes of the patient. Therefore patient-centered outcomes should also include the patient's own evaluation of the care received.

Another issue to be addressed is the lack of data elements in Electronic Medical Record (EMR) systems for capturing, reporting, and analyzing PROs. This is the key to accountability and quality improvement in mental health and substance use treatment.

Using Natural Language Processing (NLP) for the automated processing of clinical narratives

Electronic documentation in mental health and substance use treatment is often captured in the form of narrative text such as psychotherapy notes. Natural Language Processing (NLP) and machine learning tools and techniques (such as named entity recognition) can be used to extract clinical concepts and other insight from clinical notes.

Another area of interest is Clinical Question Answering (CQA) that would allow clinicians to ask questions in natural language and extract clinical answers from very large amounts of unstructured sources of medical knowledge. PubMed has more than 23 millions citations for biomedical literature from MEDLINE, life science journals, and online books. It is impossible for the human brain to keep up with that amount of knowledge.

Computer-Based Cognitive Behavioral Therapy (CCBT) and mHealth

According to a report published last year by the California HealthCare Foundation and titled The Online Couch: Mental Health Care on the Web:

"Computer-based cognitive behavioral therapy (CCBT) cost-effectively leverages the Internet for coaching patterns in self-driven or provider-assisted programs. Technological advances have enabled computer systems designed to replicate aspects of cognitive behavior therapy for a growing range of mental health issues".

An example of a successful nationwide adoption of CCBT is the online behavioral therapy site Beating the Blues in the United Kingdom which has been proven to help patients suffering from anxiety and mild to moderate depression. Beating the Blues has been recommended for use in the NHS by the National Institute for Health and Clinical Excellence (NICE).

In addition, there is growing evidence to support the efficacy of mobile health (mHealth) technologies for supporting patent engagement and activation in health behavior change (e.g., smoking cessation).

 

Technologies in support of a Collaborative Care Model

There is sufficient evidence to support the efficacy of the collaborative care model (CCM) in the treatment of chronic mental health and substance use conditions.The CCM is based on the following principles:

• Coordinated care involving a multi-disciplinary care team.


• Longitudinal care plan as the backbone of care coordination.


• Co-location of primary care and mental health and substance use specialists.


• Case management by a Care Manager. 

Implementing an effective collaborative care model will require a new breed of advanced clinical collaboration tools and capabilities such as:

• Conversations and knowledge sharing using tools like video conferencing for virtual two-way face-to-face communication between clinicians (see my previous post titled Health IT Innovations for Care Coordination).


• Clinical content management and case management tools.


• File sharing and syncing allowing the longitudinal care plan to be synchronized and shared among all members of the care team.


• Light-weight and simple clinical data exchange standards and protocols for content, transport, security, and privacy. 

 

Patient Consent and Privacy

Because of the stigma associated with mental health and substance use, it is important to give patients control over the sharing of their medical record. Patients consent should be obtained about what type information is shared, with whom, and for what purpose. The patient should also have access to an audit trail of all data exchange-related events. Current paper-based consent processes are inefficient and lack accountability. Web-based consent management applications facilitate the capture and automated enforcement of patient consent directives (see my previous post titled Patient privacy at web scale).

Health IT Innovations for Care Coordination

The Business Case

According to an article by Bodenheimer et al. published in the January/February 2009 issue of Health Affairs and titled Confronting The Growing Burden Of Chronic Disease: Can The U.S. Health Care Workforce Do The Job?:

In 2005, 133 million americans were living with at least one chronic condition. In 2020, this number is expected to grow to 157 million. In 2005, sixty-three million people had multiple chronic illnesses, and that number will reach eighty-one million in 2020. 

Patients with co-morbidities are typically treated by multiple clinicians working for different healthcare organizations. Care Coordination is necessary for the effective treatment of these patients and reducing costs. Effective Care Coordination can reduce the number of redundant tests and procedures, hospital admissions and readmissions, medical errors, and patient safety issues related to the lack of medication reconciliation. 

According to a paper by Dennison and Hugues published in the Journal of Cardiovascular Nursing and titled Progress in Prevention Imperative to Improve Care Transitions for Cardiovascular Patients, direct communication between the hospital and primary care setting occurred only 3 percent of the time. According to the same paper, at discharge, a summary was provided only 12 percent of the time, and this occurrence remained poor at 4 weeks post-discharge, with only 51 percent of practitioners providing a summary. The paper concluded that this standard affected quality of care in 25 percent of follow-up visits.

Health Information Exchanges (HIEs) and emerging delivery models like the Accountable Care Organization (ACO) and the Patient-Centered Medical Home (PCMH) were designed to promote care coordination. However, according to an article by Furukawa et al. published in the August 2013 issue of Health Affairs and titled Hospital Electronic Health Information Exchange Grew Substantially In 2008–12:

In 2012, 51 percent of hospitals exchanged clinical information with unaffiliated ambulatory care providers, but only 36 percent exchanged information with other hospitals outside the organization. . . . In 2012 more than half of hospitals exchanged laboratory results or radiology reports, but only about one-third of them exchanged clinical care summaries or medication lists with outside providers.                      

Furthermore, the financial sustainability of many HIEs remains an issue. According to another article by Adler-Milstein et al. published in the same issue of Health Affairs and titled Operational Health Information Exchanges Show Substantial Growth, But Long-Term Funding Remains A Concern, "74 percent of health information exchange efforts report struggling to develop a sustainable business model".  

There are other obstacles to care coordination including the existing fee-for-service healthcare delivery model (as opposed to a value-based model), the lack of interoperability between healthcare information systems, and the lack of adoption of effective collaboration tools.

According to a report by the Institute of Medicine (IOM) titled  The Healthcare Imperative: Lowering Costs and Improving Outcomes, a program designed to improve care coordination could result in national annual savings of $240.1 billions.

What Can We Learn From High Risk Operations in Other Industries?

Similar breakdowns in communication during shift handovers have also been observed in risky operating environments, sometimes with devastating consequences. In the aerospace industry, human factors research and training have played an important role in successfully addressing the issue. A research paper by Parke and Mishkin titled Best Practices in Shift Handover Communication: Mars Exploration Rover Surface Operations included the following recommendations:

• Two-way Communication, Preferably Face-to-Face. . . . Two-way communication enables the incoming worker to ask questions and rephrase the material to be handed over, so as to expose these differences [in mental model].



• Face-to-Face Handovers with Written Support. Face-to-face handovers are improved if they are supported by structured written material—e.g., a checklist of items to convey, and/or a position log to review. 



• Content of Handover Captures Intent. Handover communication works best if it captures problems, hypotheses, and intent, rather than simply lists what occurred.

While the logistics of healthcare delivery does not always permit physical face-to-face communication between clinicians during transitions of care, the web has seen an explosion in online collaboration tools. Innovative organizations have embraced these technologies giving rise to a new breed of enterprise software known as Enterprise 2.0 or Social Enterprise Software. This new breed of software is not only social, but also mobile, and cloud-based.

Care Coordination in the Health Enterprise 2.0

• Collaborative Authoring of a Longitudinal Care Plan. From a content perspective, the Care Plan is the backbone of Care Coordination. The Care Plan should be comprehensive and standardized (similar to the checklist in aerospace operations). It should include problems, medications, orders, results, care goals (taking into consideration the patient's wishes and values), care team members and their responsibilities, and actual patient outcomes (e.g., functional status). Clinical Decision Support (CDS) tools can be used to dynamically generate a basic Care Plan based on the patient's specific clinical data. This basic Care Plan can be used by members of the care team to build a more elaborate Longitudinal Care Plan. CDS tools can also automatically generate alerts and reminders for the care team.



• Communication and Collaboration using Enterprise 2.0 Software.  These tools should be used to enable collaboration between all members of the care team which include not only clinicians, but also non-clinician caregivers, and the patient herself. Beyond email, these tools allow conversations and knowledge sharing through instant messaging, video conferencing (for virtual two-way face-to-face communication), content management, file syncing (allowing the longitudinal care plan to be synchronized and shared among all members of the care team), search, and enterprise social networking (because clinical work is a social activity like most human activities). A providers directory should make it easy for users to find a specific provider and all their contact information based on search criteria such as location, specialty, knowledge, experience, and telephone number.



• Light Weight Standards and Protocols for Content, Transport, Security, and Privacy. The foundation standards are: REST, JSON, OAuth2, and OpenID Connect. An emerging approach that could really help put patients in control of the privacy of their electronic medical record is the OAuth2.0-based User-Managed Access (UMA) Protocol of the Kantara Initiative (see my previous post titled Patient Privacy at Web Scale). Initiatives like the ONC-sponsored RESTful Health Exchange (RHEX) project and the HL7 Fast Healthcare Interoperability Resources (FHIR) hold great promise.



• Case Management Tools. They are typically used by Nurse Practionners (Case Managers) in Medical Homes, a concept popularized by the Patient-Centered Medical Home healthcare delivery model to coordinate care. These tools integrate various capabilities such as risk stratification (using predictive modeling) to identify at-risk patients, content management (check-in, check-out, versioning), workflows (human tasks), communication, business rule engine, and case reporting/analytics capabilities.

Essential IT Capabilities Of An Accountable Care Organization (ACO)

The Certification Commission for Health Information Technology (CCHIT) recently published a document entitled A Health IT Framework for Accountable Care. The document identifies the following key processes and functions necessary to meet the objectives of an ACO:

• Care Coordination
• Cohort Management
• Patient and Caregiver Relationship Management
• Clinician Engagement
• Financial Management
• Reporting
• Knowledge Management.

The key to success is a shift to a data-driven healthcare delivery. The following is my assessment of the most critical IT capabilities for ACO success:

• Comprehensive and standardized care documentation in the form of electronic health records including as a minimum: patients' signs and symptoms, diagnostic tests, diagnoses, allergies, social and familiy history, medications, lab results, care plans, interventions, and actual outcomes. Disease-specific Documentation Templates can support the effective use of automated Clinical Decision Support (CDS). Comprehensive electronic documentation is the foundation of accountability and quality improvement.


• Care coordination through the secure electronic exchange and the collaborative authoring of the patient's medical record and care plan (this is referred to as clinical information reconciliation in the CCHIT Framework). This also requires health IT interoperability standards that are easy to use and designed following rigorous and well-defined software engineering practices. Unfortunately, this has not always been the case, resulting in standards that are actually obstacles to interoperability as opposed to enablers of interoperability. Case Management tools used by Medical Homes (a concept popularized by the Patient-Centered Medical Home model) can greatly facilitate collaboration and Care Coordination.


• Patients' access to and ownership of their electronic health records including the ability to edit, correct, and update their records. Patient portals can be used to increase patients' health literacy with health education resources. Decision aids comparing the benefits and harms of various interventions (Comparative Effectiveness Research) should be available to patients. Patients' health behavior change remains one of the greatest challenges in Healthcare Transformation. mHealth tools have demonstrated their ability to support Patient Activation.


• Secure communication between patients and their providers. Patients should have the ability to specify with whom, for what purpose, and the kind of medical information they want to share. Patients should have access to an audit trail of all access events to their medical records just as consumers of financial services can obtain their credit record and determine who has inquired about their credit score.


• Clinical Decision Support (CDS) as well as other forms of cognitive aids such as Electronic Checklists, Data Visualization, Order Sets, Infobuttons, and more advanced Clinical Question Answering (CQA) capabilities (see my previous post entitled Automated Clinical Question Answering: The Next Frontier in Healthcare Informatics). The unaided mind (as Dr. Lawrence Weed, the father of the Problem-Oriented Medical Record calls it) is no longer able to cope with the large amounts of data and knowledge required in clinical decision making today. CDS should be used to implement clinical practice guidelines (CPGs) and other forms of Evidence-Based Medicine (EBM).
  
  However, the delivery of care should also take into account the unique clinical characteristics of individual patients (e.g., co-morbidities and social history) as well as their preferences, wishes, and values. Standardized Clinical Assessment And Management Plans (SCAMPs) promote care standardization while taking into account patient preferences and the professional judgment of the clinician. CDS should be well integrated with clinical workflows (see my my previous post entitled Addressing Challenges to the Adoption of Clinical Decision Support (CDS) Systems).


• Predictive risk modeling to identity at-risk populations and provide them with pro-active care including early screening and prevention. For example, predictive risk modeling can help identify patients at risk of hospital re-admission, an important ACO quality measure.


• Outcomes measurement with an emphasis on patient outcomes in addition to existing process measures. Examples of patient outcome measures include: mortality, functional status, and time to recovery.


• Clinical Knowledge Management (CKM) to disseminate knowledge throughout the system in order to support a learning health system. The Institute of Medicine (IOM) released a report titled  Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care. The report describes the learning health system as:
  
  

  "delivery of best practice guidance at the point of choice, continuous learning and feedback in both health and health care, and seamless, ongoing communication among participants, all facilitated through the application of IT."



• Applications of Human Factors research to enable the effective use of technology in clinical settings. Examples include: implementation of usability guidelines to reduce Alert Fatigue in Clinical Decision Support (CDS), Checklists, and Visual Analytics. There are many lessons to be learned from other mission-critical industries that have adopted automation. Following several incidents and accidents related to the introduction of the Glass Cockpit about 25 years ago, Human Factors training known as Cockpit Resource Management (CRM) is now standard practice in the aviation industry.

How Checklists Can Enhance Clinical Decision Support (CDS)

I have been reading "The Checklist Manifesto", a book written by Dr. Atul Gawande on the effectiveness of checklists in healthcare delivery. Another paper recently published in the Milbank Quarterly entitled "Counterheroism, Common Knowledge, and Ergonomics: Concepts from Aviation That Could Improve Patient Safety" suggests that beyond checklists, proven aviation safety practices such as Cockpit Resource Management (CRM), Joint safety briefings, and first-names-only rules could help improve patient safety.

I first became aware of the importance of checklists while I was being trained as a Flight Engineer. I spent a lot of time studying them carefully as an aviation student. Checklists are used during normal, abnormal, and emergency situations and pilots go through practical exercises in flight simulators to use them correctly. Let's not mince words: aviation as we know it today would not be possible without checklists.

In a study entitled "Missed and Delayed Diagnoses in the Emergency Department: A Study of Closed Malpractice Claims From 4 Liability Insurers", researchers found that:

The leading breakdowns in the diagnostic process were failure to order an appropriate diagnostic test (58% of errors), failure to perform an adequate medical history or physical examination (42%), incorrect interpretation of a diagnostic test (37%), and failure to order an appropriate consultation (33%). The leading contributing factors to the missed diagnoses were cognitive factors (96%), patient-related factors (34%), lack of appropriate supervision (30%), inadequate handoffs (24%), and excessive workload (23%).

Checklists can serve as cognitive aid in helping clinicians do their job safely. While the idea of using checklists and standard operating procedures has been fully embraced and adopted by aviation professionals for more than 70 years, it is only now making inroads into the field of medicine particularly in high pressure environments like intensive care units. The use of checklists in medicine has already shown the potential to save patients live and reduce human errors. However, the main challenge remains the acceptance of checklists by clinicians concerned about "Cookbook Medicine".

Checklists are just cognitive aids and the presence of an experienced and competent professional will always make a big difference in critical situations. As Captain Sullenberger (the airline pilot who successfully ditched US Airways Flight 1549 in the Hudson River in New York City, on January 15, 2009) said, "One way of looking at this might be that for 42 years, I've been making small, regular deposits in this bank of experience: education and training. And on January 15 the balance was sufficient so that I could make a very large withdrawal."

On modern airplanes, Electronic Centralised Aircraft Monitor (ECAM) systems or Engine Indicating and Crew Alerting Systems (EICAS) monitor aircraft systems and engines and displays messages in case of failure, as well as recommended remedial actions in the form of checklists. The National Transportation Safety Board (NTSB) accident report on US Airways Flight 1549 indicates that the First Officer "was able to promptly locate the [Engine Dual Failure checklist] procedure listed on the back cover of the [Quick Reference Handbook] QRH, turn to the appropriate page, and start executing the checklist."

In medicine, factors such as comorbidity can complicate the design of effective CDS. However, with the explosion of medical knowledge and evidence-based guidelines, CDS will become an essential tool in healthcare delivery. The design, development, implementation, and use of CDS is knowledge-intensive and require an effective collaborative knowledge management strategy. The challenge will be to integrate checklists into the different CDS modalities such as context-sensitive Infobuttons, order sets, alerts, reminders, data entry and visualization, and clinical workflows.

For example, the evaluation results (in the form of recommendations) of a CDS rule can be presented to the clinician as an electronic checklist. This in turn can be tied directly to quality measures in the era of Meaningful Use, Pay-For-Performance, and Accountable Care Organizations (ACOs). An interesting example would be a checklist that prompts clinicians to generate detailed discharge instructions to satisfy quality measures for patients with heart failure or acute myocardial infection.

There is an important Human Factors aspect to the design and use of cockpit checklists and flight-deck procedures. This has been the subject of advanced research at NASA Ames Research Center more than twenty years ago and the results have been widely disseminated and implemented in the aviation industry.

In an article entitled "The Checklist" published in the New Yorker, Atul Gawande wrote:

"But consider: there are hundreds, perhaps thousands, of things doctors do that are at least as dangerous and prone to human failure as putting central lines into I.C.U. patients. It’s true of cardiac care, stroke treatment, H.I.V. treatment, and surgery of all kinds. It’s also true of diagnosis, whether one is trying to identify cancer or infection or a heart attack. All have steps that are worth putting on a checklist and testing in routine care. The question—still unanswered—is whether medical culture will embrace the opportunity."

Peter Pronovost, an intensivist at Johns Hopkins Hospital and pioneer in the use of checklist in medicine, implemented a checklist at 127 Michigan intensive care units (ICUs) to reduce catheter-related blood stream infections (CRBSI). The project was so successful that it is estimated that it could significantly reduce the 28,000 deaths and 3 billion dollars in costs caused by these hospital-acquired infections.

The HL7 Clinical Decision Support (CDS) workgroup is working on standards for the vMR (Virtual Medical Record), Infobuttons, and Order Sets. There is also an effort at the OMG to publish a Clinical Decision Support Services specification for service-oriented CDS capabilities. The Flight Operation Interests Group (FOIG) of the Air Transport Association (ATA) is developing a data model and XML Schema for flight deck procedures and checklists. Developing a shareable content model for checklists in medicine could be an interesting idea.